Transitioning from an individual contributor to a managerial role is one of the most significant shifts a software developer can make. While technical skills remain important, becoming an engineering manager requires a different set of capabilities—leadership, communication, and strategic planning. This transition can be challenging, but with the right preparation and mindset, it’s a highly rewarding step in a developer’s career.

Understanding the Shift in Responsibilities

When you were a developer, your focus was coding, problem-solving, and delivering solutions. As an engineering manager, your primary responsibilities now shift towards guiding teams, developing talent, and ensuring the alignment of engineering goals with company objectives.

In this new role, you’ll need to let go of the hands-on tasks that once defined your daily work. The focus moves to managing the people who execute the technical work and empowering them to succeed.

Your new day-to-day activities include running meetings, setting goals, coordinating projects, and addressing challenges related to team dynamics. According to a study by Gallup, managers being actively involved in team development lead to a 59% reduction in turnover. This emphasizes the critical nature of leadership in retaining top talent.

Building New Skills for Management

As you transition into management, you must cultivate new skills to complement your technical background. These include communication, conflict resolution, time management, and strategic planning. But most importantly, you’ll need to focus on leadership.

Leadership and Communication

Leadership in engineering isn’t just about making decisions—it’s about guiding a team through challenges and setting a clear direction. Communication breakdowns or a lack of communication skills are believed to contribute to 86% of workplace failures. 

Engineering teams rely heavily on collaboration across different functions—developers, product managers, designers, and QA teams must work in sync. A coding team leader with strong communication skills can prevent bottlenecks, align team efforts toward common objectives, and provide timely feedback, which fosters continuous improvement. 

Time Management

Balancing competing priorities is another critical skill. While you may still want to code occasionally, you must allocate time efficiently across managerial duties like performance reviews, one-on-ones, and project oversight. Adopting frameworks like the Eisenhower Matrix can help you distinguish between urgent and important tasks, keeping you focused on what matters most.

Assessing Technical Skills

With the move to engineering management comes another responsibility: building your team. 

In tech organizations, hiring the right talent is critical for project success and innovation. By developing the ability to evaluate technical competencies and soft skills, managers can ensure that candidates not only meet the technical demands of the role but also align with the team’s culture and values. To do this, managers should focus on structured interview processes, use technical assessments such as coding challenges, and involve team members in the evaluation process. Learning to assess candidates holistically enables engineering managers to make informed decisions, leading to better hires and a more cohesive, productive team.

Overcoming Common Challenges

Transitioning into management comes with inevitable challenges. According to research by Harvard Business Review, 60% of new managers fail within their first two years due to inadequate preparation. Consider preparing for these two common challenges and learning how to overcome them.

Letting Go of the Code

One of the most common challenges for developers moving into management is letting go of coding. Many developers struggle with not being directly involved in day-to-day technical tasks. While it’s tempting to dive back into code to solve a problem or fix a bug, it’s important to remember that your focus should now be guiding your team and enabling them to solve these challenges.

Handling Conflict and Team Dynamics

Another common challenge is managing conflict and maintaining team morale. Engineering teams often have diverse personalities and skill sets, which can lead to tension. Managers must constructively mediate conflicts, ensuring issues are resolved without disrupting productivity. Effective conflict resolution helps maintain a positive work environment and contributes to a more cohesive and collaborative team.

Balancing Technical and Managerial Tasks

In your new role, you’ll often feel pulled between technical tasks and managerial responsibilities. Striking the right balance is key to your success as an engineering manager.

Delegation

One of the most powerful tools for a manager is delegation. While you may have been the go-to person for solving technical challenges, it’s now your job to empower your team to solve these issues themselves. Delegating tasks frees up your time for more strategic duties and fosters team growth and ownership.

Staying Technically Proficient

At the same time, staying current with industry trends and technologies is important. Engineering managers must know emerging tools and methodologies to ensure their team works efficiently. Set aside time for technical learning, whether it’s through attending conferences, reading industry blogs, or engaging in periodic coding tasks.

Tips for a Successful Transition

1. Find a Mentor: Seek an experienced engineering manager to guide you through the transition. Having someone to turn to for advice on leadership challenges can be incredibly valuable.
2. Set Clear Expectations: Make sure your team understands your role and responsibilities. Clarify that while you may not be coding as much, you are there to support their technical development and ensure the team’s success.
3. Stay Organized: Use project management tools to manage both your managerial and technical tasks. Staying organized will help you balance your time more effectively and ensure nothing slips through the cracks.
4. Emphasize Team Development: Encourage team members to take on challenges and grow. Providing opportunities for skill development and career growth is one of the best ways to keep your team engaged and productive.

Conclusion: Embracing the New Role

Transitioning from developer to engineering manager is a rewarding yet demanding journey. You can become a successful and effective manager by developing new leadership skills, maintaining technical proficiency, and fostering a positive team environment. Remember, the goal is to lead and empower your team to succeed and grow, setting the team and the company up for long-term success.

The post Engineering Leadership: Transitioning from Developer to Manager appeared first on HackerRank Blog.

Artificial intelligence (AI) is here, and it’s changing the game in virtually every industry. Whether it’s predicting market trends, automating tedious tasks, or providing personalized customer experiences, AI’s vast potential has proven to be a boon for businesses ready to embrace it.

However, as with any transformative technology, adopting AI isn’t as simple as flipping a switch. The rise of AI has created an enormous demand for professionals with top AI skills, resulting in a widening AI skills gap. Recent research from Salesforce shows that, while over half of U.S.-based senior IT leaders say their business is currently using or experimenting with AI, 66% say their employees don’t have the skills to leverage the technology successfully. As a result, companies are racing to fill roles in AI, machine learning, and data science, often facing fierce competition and high costs in their search for talent.

But there’s a solution that’s both efficient and effective: upskilling. Instead of dedicating valuable HR resources battling it out for AI talent, why not invest in the team you already have? Upskilling your existing workforce not only enables you to leverage AI technologies more rapidly but also promotes employee growth and retention — a win-win scenario for forward-thinking companies.

In this post, we’ll explore the top AI skills your team needs in 2023 and provide actionable advice on how you can facilitate learning and development in these areas. With these insights, you can develop a plan for building a team that’s prepared for anything our AI-driven future might bring.

Programming Skills

In the world of AI, programming serves as the bedrock, giving us the means to instruct computers to perform complex tasks. Among the plethora of programming languages, Python stands out in the AI community due to its readability and the powerful libraries it offers for various AI tasks, like TensorFlow, PyTorch, and Scikit-learn, Pandas, NumPy, and Keras. Additionally, R, with its strong suit in statistical analysis and data visualization, is popular choice, while other languages like Java, C++, and Julia have their specific applications.

Understanding these languages and their associated libraries paves the way for efficient algorithm creation, seamless data handling, and effective model training — skills fundamental to AI. Furthermore, tools that facilitate AI development, such as Jupyter Notebooks for code sharing and Google Colab for high-performance computations, can significantly enhance productivity.

To bolster these programming skills, consider workshops, online coding platforms, and providing resources to learn relevant languages and libraries. Remember, programming is a hands-on skill. Encouraging an environment of experimentation and learning by doing can make a world of difference.

Linear Algebra and Statistics

While it’s possible to use AI tools and libraries without deep mathematical knowledge, understanding the underlying principles of linear algebra and statistics can empower your team to work more effectively with AI. These mathematical domains are the backbone of many AI algorithms, and familiarity with them can lead to more innovative problem solving and a deeper comprehension of the AI development process.

Linear algebra — encompassing vectors, matrices, and the operations that can be performed with them — is fundamental to areas such as deep learning and computer vision. On the other hand, statistics is vital for interpreting data, making predictions, and validating models, all of which are central to machine learning and data science.

By reinforcing mathematical skills in linear algebra and statistics, your team can gain a stronger command of AI technologies and a more nuanced understanding of the results they produce. A solid grounding in these areas can be fostered through online courses, textbooks, or even bringing in a subject-matter expert for a series of workshops.

Natural Language Processing (NLP) and Question Answering

As AI ventures beyond the realms of numbers and begins to understand and interact in human language, natural language processing (NLP) has emerged as a crucial AI skill. NLP involves teaching machines how to understand, analyze, generate, and respond to human language in a valuable way. 

From customer service chatbots to sentiment analysis, from language translation to voice assistants like Siri or Alexa, NLP is the magic that makes these tools understand and respond to human language accurately. 

Question answering (QA) is a subset of NLP and aims to provide precise answers to specific questions asked in natural language. It’s the technology behind tools like Google’s search engine, which can provide direct answers to users’ queries.

A solid foundation in NLP and QA can open new avenues for your business and drastically improve customer interaction. To build competency in these areas, encourage your team to explore online courses and hands-on projects that focus on NLP and QA techniques. These can include tasks such as building a simple chatbot or developing a sentiment analysis tool.

Machine Learning

Machine learning (ML) stands as one of the pillars of AI. ML teaches machines how to learn and make decisions from data, enabling them to perform tasks without explicit programming. From predictive models in finance to recommendation systems on e-commerce platforms, ML is transforming the way we interact with the digital world. 

Here are some important ML skills to focus on:

• Deep Learning: A subset of ML that models high-level abstractions in data using artificial neural networks. It’s the driving force behind advanced AI applications like voice recognition and image classification.
• Recommender Systems: These are algorithms that suggest products or services to users based on their behavior. They’re crucial in industries like retail, entertainment, and social media, helping to personalize user experiences.
• Computer Vision: This involves teaching machines to “see” and understand visual data. It’s integral to applications such as facial recognition, autonomous vehicles, and medical imaging.
• Classification: This is the process of predicting the category of a given input. It’s widely used in areas like spam detection, customer churn prediction, and disease diagnosis.
• Reinforcement Learning: A type of ML where an agent learns to make decisions by interacting with its environment. It’s key in developing systems that can learn complex behaviors, like game playing or autonomous driving.

To empower your team with ML skills, look for online courses that cover these areas, and prioritize practical projects that allow your team to apply what they’ve learned. Encourage a culture of continuous learning and knowledge sharing, ensuring that everyone stays on top of the rapidly evolving ML landscape.

AI Ethics and Bias

As AI technologies increasingly influence our lives and decisions, the need for ethical AI systems has become paramount. AI ethics deals with ensuring that AI technologies are developed and used responsibly, respecting human rights and societal norms.

One of the major challenges in AI ethics is handling bias. AI systems learn from data, and if this data contains biased information, the AI system will likely reproduce these biases. Bias in AI can lead to unfair outcomes, ranging from discrimination in hiring processes to inequity in loan approvals. 

Therefore, learning how to detect and mitigate bias in AI is critical. Bias detection and mitigation involve exploring the data, identifying potential biases, and applying various techniques to reduce the effect of these biases on the AI model’s decisions.

Training in AI ethics and bias can help your team create fair, transparent, and accountable AI systems. Encourage your team to participate in ethics training programs, read key literature on the topic, stay up to date on the latest legislation and regulations, and regularly discuss ethical considerations and bias challenges as a part of the AI development process.

Cloud and Edge AI

As AI applications become increasingly data intensive, cloud and edge AI have risen to prominence. They represent two different but complementary approaches to running AI algorithms.

Cloud AI refers to AI systems that run on cloud servers, which provide virtually limitless computing power and storage. It allows companies to scale their AI capabilities easily, manage large volumes of data, and access advanced AI services provided by cloud platforms.

On the other hand, edge AI involves running AI algorithms directly on devices (like smartphones, IoT devices, etc.) or at the “edge” of the local network. This approach is becoming increasingly popular as it enables real-time data processing, reduces data transmission costs, and enhances privacy since sensitive data doesn’t need to leave the device.

Understanding cloud and edge AI will help your team make strategic decisions about where and how to run your AI applications. Upskilling in these areas could involve training on popular cloud platforms, learning about edge computing architectures, and experimenting with developing and deploying models in different environments.

Explainable AI 

As AI systems become more complex, understanding why they make certain decisions is both challenging and crucial. This is where explainable AI (XAI) comes into play. XAI is all about making AI decisions transparent, understandable, and justifiable.

Why does this matter? Imagine an AI system denied a loan application but couldn’t explain why. Without understanding the reasoning behind AI decisions, it’s hard to trust them. Moreover, explainability is essential for diagnosing and fixing issues in AI models.

Understanding XAI principles and techniques allows your team to create AI systems that are not only intelligent but also transparent and trustworthy. To foster skills in XAI, consider incorporating explainability as a key part of your AI development process and utilizing tools and techniques that promote explainability in AI. Online resources and practical exercises on XAI can also be beneficial.

Signal Processing

Signal processing is the art and science of modifying and analyzing signals such as sound, images, and sensor data. In the context of AI, signal processing techniques are invaluable in tasks like speech recognition, image and video processing, and sensor data analysis.

Consider how voice assistants like Siri or Alexa work. They use signal processing techniques to convert your voice (an audio signal) into a format that an AI algorithm can understand. Or think about how a self-driving car uses sensors to perceive its environment — the data from these sensors is processed and analyzed to make driving decisions.

To bolster your team’s signal processing skills, consider workshops or online courses that cover the fundamentals of signal processing along with hands-on projects. Encourage your team to experiment with signal processing in different contexts, helping them understand its practical applications in AI.

Big Data

AI thrives on data — the more, the better. As businesses continue to generate and capture vast amounts of data, knowing how to manage and extract value from this “Big Data” has become a crucial AI skill.

Big Data refers to data sets that are too large or complex to process using traditional data processing methods. It’s not just about volume but also variety (different types of data) and velocity (the speed of data generation and processing). 

Big Data skills include understanding distributed storage (like Hadoop), querying tools (like SQL and NoSQL), and data processing frameworks (like Spark). These tools allow your team to handle large-scale data, perform complex computations, and ultimately feed your AI models with the high-quality, diverse data they need to function effectively.

Building Big Data skills often involves hands-on experience with relevant tools and platforms. Consider encouraging your team to take on projects that involve large, diverse datasets or offering training in the key tools used in Big Data management.

AI Delegation

As AI systems become more sophisticated, they’re taking on an increasing number of tasks. This trend leads to an emerging AI skill: AI delegation. This skill involves understanding what tasks to delegate to AI and how to manage these AI-powered processes effectively.

AI delegation is about more than just automating tasks. It’s about leveraging AI to enhance productivity, decision making, and creativity. It involves identifying which tasks AI can perform efficiently (e.g., data analysis, pattern recognition), and which tasks should be left to humans (e.g., tasks requiring emotional intelligence, complex judgment, or creative thinking).

Understanding AI capabilities and limitations can help leaders effectively delegate tasks, saving time and resources while maintaining or improving quality. Fostering these skills can be as simple as staying informed about AI advancements, experimenting with AI tools in different tasks, and fostering a culture that is open to adopting AI solutions.

Key Takeaways

AI has permeated every industry, and its value in solving complex problems, automating tasks, and generating insights is undeniable. However, harnessing its full potential requires an array of skills, from programming and math to understanding AI ethics and knowing how to delegate tasks to AI.

Upskilling your team in these top AI skills can pave the way for innovative solutions, increased efficiency, and a competitive edge. Remember that learning is an ongoing journey, especially in a rapidly evolving field like AI. Cultivate an environment that encourages continuous learning and hands-on experience with AI technologies. 

While the prospect of upskilling your team in AI might seem daunting, the rewards in terms of business performance, employee satisfaction, and market competitiveness make it a worthwhile investment. So, whether you’re just starting your AI journey or looking to take your capabilities to the next level, focusing on these top AI skills will set your team — and your company — up for success.

This article was written with the help of AI. Can you tell which parts?

The post Top 10 AI Skills to Upskill Your Workforce in 2023 appeared first on HackerRank Blog.

In the world of tech, the only constant is change, and this is especially true within the realm of data science. This discipline evolves at such a lightning pace that what was cutting-edge a few years ago is considered commonplace — or even antiquated — today. In fact, according to the World Economic Forum, 50% of all employees will need reskilling by 2025 as the adoption of technology increases.

As a tech leader, hiring manager, or recruiter, it’s important to not just hire for the right skills — particularly at a time when 60% of hiring managers say data science and analytics roles are the toughest to hire for. It’s also critical to continuously invest in your team’s development. It’s not about playing catch-up with the latest tech trend but about staying on the wave of evolution, ready to ride its crest. 

In 2023, upskilling your data science team isn’t just a nice-to-have but a need-to-have strategy. The benefits of this upskilling strategy are multifold: not only does it future-proof your organization but it also increases your team’s productivity, lowers turnover, and helps maintain a competitive edge in the market.

So, whether you’re hoping to dive deeper into machine learning, harness the latest in artificial intelligence, or make the most of data visualization tools, this blog post is your guide to upskilling your data science team effectively and efficiently. With a strong upskilling strategy, your data science team will be prepared to navigate the future of this exciting, fast-paced industry for years to come.

Why You Should Upskill Your Data Science Team

According to the U.S. Bureau of Labor Statistics, data science jobs are expected to grow at a rate of 36% between now and 2031 — far faster than the 5% average growth rate for all occupations. This rapid rise in demand is also creating a shortage of data science talent, making upskilling an increasingly appealing — and necessary — strategy. But its benefits extend beyond simply filling in the skills gap. 

Firstly, upskilling increases productivity. An up-to-date, well-equipped data scientist will be more efficient, better able to troubleshoot issues, and more likely to find innovative solutions. It’s simple – if your team has a better understanding of the tools at their disposal, they will be more effective at their jobs. 

Secondly, investing in your team’s growth can also have a positive impact on employee satisfaction and retention. A LinkedIn report shows that 94% of employees would stay at a company longer if it invested in their learning and development. Upskilling gives your data scientists a sense of professional progression and satisfaction, which translates to a more committed and stable team.

Lastly, but importantly, upskilling keeps you competitive. The field of data science is racing ahead, with advancements in AI, machine learning, and big data analytics becoming commonplace. Businesses not only need to keep up, but they also need to be ready to leverage these advancements. A data science team that is proficient in the latest technologies and methodologies is a huge competitive advantage.

In essence, upskilling your data science team is about more than just learning new skills. It’s about fostering a culture of continuous growth and learning, which enhances your team’s capabilities, morale, and ultimately, your organization’s bottom line.

Determining the Skills Gap

Before you can effectively upskill your data science team, you need to identify your skills gaps. This involves both a high-level overview of your team’s capabilities and a deep dive into individual competencies.

Start by reviewing your current projects and pipelines. What are the common bottlenecks? Where do the most challenges or errors occur? Answers to these questions can shed light on areas that need improvement. For instance, if your team frequently encounters difficulties with data cleaning and preprocessing, it may be beneficial to focus on upskilling in this area.

Next, look at the individual members of your team. Everyone has their own unique set of strengths and weaknesses. Some may be fantastic with algorithms but could improve their communication skills. Others might be proficient in Python but not as adept with R. You can identify these individual skill gaps through regular performance reviews, one-on-one check-ins, or even anonymous surveys. 

Remember, the goal here is not to criticize or find fault but to identify opportunities for growth. The process of determining the skills gap should be collaborative and constructive and should empower team members to take ownership of their professional development.

Once you have a clear picture of the skills gaps in your team, you can start to strategize about the most effective ways to bridge these gaps. Whether it’s through online courses, in-house training, or peer-to-peer learning, the key is to create a supportive environment that encourages continuous learning and improvement.

Key Skills to Invest in 2023

With a clear understanding of where your team stands, let’s now focus on the pivotal data science skills that your team should be honing in 2023.

• Advanced Machine Learning and AI: Machine learning and AI continue to dominate the data science field, with technologies becoming more advanced and integrated into a myriad of applications. Upskilling in areas like deep learning, reinforcement learning, neural netorks, and natural language processing can give your team a significant advantage.
• Cloud Computing: With the increasing amount of data being generated, cloud platforms like AWS, Azure, and Google Cloud are becoming increasingly essential. Cloud computing skills can enable your team to handle large datasets more efficiently and perform complex computations without heavy investment in infrastructure.
• Data Visualization: The ability to communicate complex results through intuitive visuals is crucial. Tools like Tableau, PowerBI, and Python libraries such as Matplotlib and Seaborn are continually evolving. Therefore, keeping up to date with these tools can help your team better communicate their findings and make data-driven decisions more accessible to stakeholders.
• Ethics in AI and Data Science: As AI and data science technologies become more advanced and pervasive, ethical considerations become even more critical. Understanding bias in datasets, privacy issues, and the ethical implications of AI decisions will be an important skill for the foreseeable future.
• Communication and Storytelling: A great data scientist isn’t just someone who can crunch numbers but someone who can explain what those numbers mean. Good storytelling helps translate the complex into the understandable, turning raw data into actionable insights. In 2023, soft skills like communication and storytelling continue to be in high demand alongside technical expertise.

While the technical skills needed can vary depending on your industry and specific company needs, these are areas that are becoming universally important in data science. Providing opportunities to upskill in these areas can ensure your team remains adaptable and ready to tackle the future of data science.

Upskilling Strategies

Now that we’ve highlighted the importance of upskilling and outlined the key skills to invest in for 2023, let’s discuss some effective strategies to upskill your data science team.

• Online Courses and Certifications: The internet is a treasure trove of learning resources, with platforms like Coursera, edX, and Udacity offering specialized courses in data science. These platforms offer up-to-date courses in partnership with leading universities and tech companies, ensuring your team gets quality and relevant learning materials. Encouraging your team to pursue relevant certifications can be a great way to upskill.
• Mentoring and Peer Learning: Internal mentoring programs where less experienced team members learn from their more experienced colleagues can be an effective way to transfer knowledge and skills. Similarly, encouraging peer learning — perhaps through coding challenges or pair programming sessions — can foster a healthy learning culture within your team.
• In-house Workshops and Seminars: Organizing in-house workshops on critical topics can be another excellent way to upskill your team. These can be led by team members who have a strong grasp of a particular area or by external experts. Regular seminars keep the team updated about the latest trends and advancements in data science.
• Participation in Data Science Communities and Forums: Online communities like Kaggle, GitHub, or Stack Overflow are places where data scientists from all over the world share their knowledge and learn from each other. Encouraging your team to participate in these communities can expose them to a diverse range of problems, solutions, and innovative ideas.

Remember, the goal of these strategies is not just to teach your team new skills but also to cultivate a culture of continuous learning. When your team sees upskilling as a valuable, ongoing process rather than a one-time task, they’ll be better equipped to keep up with the rapidly changing field of data science.

Measuring Success and Tracking Progress

With the strategies in place and the team ready to plunge into upskilling, the next important step is to track the progress of these initiatives. How do you know if your upskilling efforts are effective? Here are some ways to measure success:

• Improvement in Project Outcomes: As your team members start applying their newly acquired skills, you should observe noticeable improvements in the quality of work and efficiency. It could be faster processing times, more accurate models, or clearer data visualizations.
• Increased Efficiency: Upskilling should make your team more autonomous and efficient. This can look like bringing tasks in-house that were previously outsourced or realizing efficiency gains in tasks that once took a long time. 
• Feedback from Team Members: Regularly check in with your team. Are they finding the upskilling initiatives useful? How do they feel about their progress? Their feedback can provide valuable insights into what’s working and what needs improvement. 
• Skill Assessments: Regular skill assessments can help measure the level of improvement in the specific skills targeted by the upskilling initiative. This can be done through quizzes, presentations, or project-based assessments.
• Retention Rates: As mentioned earlier, employees are likely to stick around longer if they feel the company is investing in their growth. So, consider monitoring turnover rates before and after implementing the upskilling initiatives. A decrease in turnover can be a good indication that your upskilling efforts are successful.

Remember, the goal of tracking progress is not to introduce a punitive or high-pressure environment but to better understand how the team is evolving. Celebrate the wins, and take the challenges as opportunities to refine your upskilling strategy. The journey to upskilling your data science team is iterative and adaptive, just like the data science discipline itself.

Preparing for the Future With Upskilling

Navigating the ever-changing landscape of data science might seem daunting, but with a systematic approach to upskilling, your team will be ready to not only weather the storm but also ride the waves of change.

Upskilling your data science team isn’t just about staying current — it’s about looking ahead and being prepared for what’s coming. It’s about creating a team that’s resilient, adaptable, and always ready to learn. It’s about setting the pace, not just keeping up with it. 

So, as a tech leader, recruiter, or hiring manager, remember that the key to a successful data science team lies not just in hiring the right people but also in continuously investing in their growth. Provide them with the tools, resources, and opportunities to learn and improve, and you’ll have a team that’s not just prepared for the year ahead, but also for the many exciting developments that lie beyond.

This article was written with the help of AI. Can you tell which parts?

The post How to Upskill Your Data Science Team in 2023 appeared first on HackerRank Blog.

The following post was originally published on Atlassian's blog, by Sarah Goff-Dupont.

Demotions are uncommon these days. More often than not, poor performers are simply let go or move on of their own accord. Voluntary demotions, however, are downright rare.

According to a study by staffing firm OfficeTeam, only 6 percent of demotions are voluntary. But according to a highly unscientific survey I conducted on LinkedIn recently, an increasing number of people are self-selecting out of management. Or at least wistfully contemplating it.

The desire to master your craft is one reason people are stepping down the corporate ladder. And although leaving management is no longer considered a career-ending move, it doesn’t have to become the Next Big Thing, either. If we shift our collective mindset around recruiting and supporting new managers, execs have a fighting chance at solving the wicked problem of leaders who won’t lead.

Why voluntary demotions are a thing in the first place

Some people find that although they enjoy certain aspects of managing a team (or team of teams), they miss doing the work they used to do. Most managers spend as much, or more, time in meetings as they do on the deep work that grows their expertise in their field. Although most people know this going in, experiencing it can still be jarring and some never make their peace with it.

While the meeting-heavy nature of managing might be nobody’s fault per se, companies do tend to make things harder on managers than necessary. On the surface, it seems logical to recruit your top performers into management roles. But wait. Just because someone is a great coder, for example, doesn’t mean they have any aptitude for leadership. (Also, why encourage your best coder to stop coding?) Without proper onboarding, training, and support—which is rare, compounding the problem—newly-minted managers often struggle in their role.

Toeing the company line takes a toll as well. “Managers have to navigate decisions every day, many of which force them to subordinate, compromise, or even violate their own personal values,“ says Sondra Norris, Senior Culture Consultant at SLAP, a management consulting firm based in San Francisco. “It may be a small thing like asking someone to work late or a big thing like laying someone off. Even if there are good reasons, it’s a paper cut to the soul.” As those little cuts accumulate over time, you bleed out.

Rethinking career paths and the definition of success

At the same time that we’re realizing management isn’t all it’s cracked up to be, a growing number of people are realizing it’s not the only way to be influential. “I worked my way up to senior management level and have studiously worked my way back to individual contributor,” says Mike Rathwell, an IT admin at Weedmaps. “I found that having been a senior manager gave me a different perspective and approach to being an individual contributor: namely, that one can still be a leader.”

As organizations have become more networked and collaborative (vs. hierarchical and siloed), we’re working across job functions more often. Thus, ICs can influence colleagues in other departments not by controlling what they do, but by rallying stakeholders around shared goals and coordinating efforts to reach them.

Nor is management the only way to be successful, even by traditional definitions of success like salary and stock options. Matt Hodges, Head of Marketing at Loom (and former Atlassian) has seen this first-hand. “A high-performing individual contributor can make just as big an impact as a high-performing manager, and companies are starting to realize this.” He cautions against assuming that people who aren’t interested in management simply want to coast their way to retirement. “Companies should ensure they have career paths, with adequate compensation opportunities, for people who want to grow and excel in their individual contributor roles.”

In the context of the ever-escalating fight to recruit and retain talented people in a labor market where labor has the upper-hand, the message is clear: if you don’t show your top ICs the love, somebody else will.

How to volunteer for a demotion

First, don’t let the word “demotion” freak you out. You may not think of your transition from manager to individual contributor (IC) as a step down, but you’ll probably hear your friends and colleagues use the D-word when you talk to them about it. Here’s the thing, though: when you demote yourself by choice, there’s no shame in it. Zilch. Zero. If you choose this path, own that choice and walk it with your head held high.

Before starting a conversation with your manager, make sure this is really the right thing for you. Once you step out of a management role, you’re unlikely to have the opportunity to lead a team again, at least as long as you’re with the same company. Ask yourself these questions:

• Am I more stressed and/or grouchy as a manager than I was as an individual contributor? If so, am I certain the management role is to blame?
• Am I prepared to have less influence over strategy and decisions, knowing that I’ll need to rally behind them even when I would’ve chosen differently?
• Does the prospect of digging into my craft again make me giddy with anticipation?
• Do I understand the career path for individual contributors in my role, and does it appeal to me?

If you answer “yes” to all or most of these, returning to an IC role is probably a solid choice.

You may need to put your request in writing at some point, but start with a verbal conversation about stepping down.

Your 1-on-1 meeting with your manager is a good forum. Otherwise, schedule dedicated time to meet privately. Get right to the point and tell them you’d like to return to an IC role. Naturally, they’ll ask why – be sure to come prepared with your list of reasons so you can lay out the full context. Also, be prepared to show that you’ve thought through which role and team you’d like to move into, how to time the transition, and who might fill your manager role.

If your manager is open to the idea, expect it to take a few days or weeks before any decisions are finalized. Remember, there is no guarantee things will go your way. So before you have the initial conversation, map out your “plan B.” Will you stay in your role and find a way to make it more palatable? Look for management opportunities in a different department? Look for IC roles at other companies? There’s no wrong answer here. Just make sure you have one.

What execs can do to stem the tide

If you’re a senior leader, you’re probably squirming in your seat by now. No matter how dedicated to supporting your employees you are, voluntary demotions are a hassle. Getting someone settled in their new IC role, backfilling their management role, and onboarding that person is a big investment.

Preventing this trend from sweeping your company starts with the way you recruit managers, especially when promoting from within. “There’s a lot of pressure to fill the empty slot,” Norris points out. But resist the temptation to fill it with someone who gives off a caustic vibe, even if they have a great managerial pedigree. A less-experienced person who can build strong relationships will perform better in the long run. “At the end of the day,” Norris says, “that relationship is what makes the team stand behind their manager, not just comply.”

Once hired, first-time managers need extra training and support – something they rarely get today. “While I learned a lot, I didn’t get the training I needed when I first became a team lead,” reports marketing strategist Shannon Titus (and several other former managers I spoke to). “It was hard to be successful for several years.” Along with onboarding tactics like a 90-day plan and training on mentorship and difficult conversations, Norris advises clients to think more broadly about their company’s managerial culture.

“The bar is getting raised with the Millennial generation coming in,” she says. “They expect to have a voice, to have easy access to whatever information they need, and to be working toward a higher purpose for themselves and the world.”

In other words, companies must create a culture of open, two-way communication and meaningful work. Managers also need to feel safe voicing concerns and owning their mistakes. Executives can set the tone by being the first to admit when things don’t go well and proving that they’ll take up good ideas no matter where they come from.

Closing Thoughts

Finally, managers at every level need a healthy work-life balance. Commuting during peak hours and the pressure to be “always on” take a massive toll. Giving them the flexibility to work from home or commute outside rush hour, along with making sure they use their vacation time – which means unplugging 100% from work – go a long way toward preventing burnout.

Like skydiving, managing a team shows up on a lot of bucket-lists. And, like skydiving, one go at it might turn out to be all that you need. Think of voluntary demotions as a reserve parachute: if things don’t go as planned, there’s no need to crash and burn.

The post Could a Voluntary Demotion Be the Best Thing for Your Career? appeared first on HackerRank Blog.

Self-taught coding, moving states, and HackerRank Community. These are the three factors that helped Jessica Byrne make a career change into tech to become a software developer.

“When I moved from Denver to Portland, I put the query “engineer” into Indeed. Almost all of the job listings were for software engineers,” said Jessica, “I realized that the market was clamoring for more developers.”.

After graduating from college, Jessica was set on finding a job in mechanical engineering - after all, it was the field she studied in school. For a moment she was seriously considering pursuing a career in modeling prosthetics.

It was her first internship as a systems engineer where Jessica started to teach herself how to code. During that four-month internship, Jessica built a dashboard that tallies and presents product issues that customers report. In order to build that dashboard, Jessica learned SQL to retrieve the information out of the database, Python to aggregate the data, and experimented with different UIs to display the data. By the end of her internship, Jessica had a love for solving new problems and enough confidence to explore a different career path.

“I enjoyed the fact that every day there was a new problem, and I appreciated that there were communities online to help answer tricky questions,” Jessica said, “It wasn’t about solving homework questions; these were real-world applications that I was trying to riddle through.”

Why Jessica decided to become a software developer

Near the end of her internship, Jessica was preparing to move from Colorado to the West Coast. After realizing that Portland had a large software developer market, Jessica decided to bet on her coding skills and start applying for software engineering jobs. Once she started landing a steady stream of interviews, Jessica discovered a trend. A lot of the companies she applied to were using HackerRank for Work to interview candidates. So she decided that she should become more familiar with the platform.

“After going through some interviews, I started solving problems through HackerRank for practice. It forced me to think about different test scenarios and how to optimize my solution.”

During her job hunt, Jessica would spend an hour or two every day studying up on coding with resources like Cracking the Coding Interview and practicing on HackerRank Community. Eventually, the studying paid off and Jessica landed her first software developer job. Today, Jessica is a software developer at Walmart Labs, where she is satisfied and challenged designing solutions for their large scale eCommerce platform.

“Hundreds of thousands of people interact with software that I have a hand in creating, which is a very rewarding experience,” says Jessica.

If you want to make a career change into engineering, but you don’t have a background in coding, scroll down to see how HackerRank Community helped Jessica change careers.

How Jessica used HackerRank Community to land a software engineering role

HackerRank (HR): In your blog, you share all the different resources you used to study computer science and prepare for technical interviews. You listed HackerRank as a resource. How did HackerRank help you prepare for technical interviews?

Jessica Byrne (JB): Tackling problems on HackerRank helps exercise the critical thinking muscles you need in a technical interview.

HR: Are there any challenges in the HackerRank Community that you found particularly helpful?

JB: One of the most helpful learning exercises for me was to attempt a HackerRank problem and then look up how others solved it. I slowly dissected other solutions to try to optimize mine and make it cleaner and more efficient.

HR: What advice about using HackerRank would you give to someone who is trying to learn to code for the first time?

JB: Try to solve one problem every day. It can feel overwhelming at first, but over time you get better at approaching problems. HackerRank also has difficulty levels that make it easy to get started and then work your way up to more difficult solutions.

HR: What advice would you give to someone who is currently in a non-tech field, but wants to break into the developer world?

JB: Coding can be frustrating. You can spend days wrestling with problems that feel like they will never be solved. However, the joy is when the knot slowly unravels and you get to the solution. My advice is to be prepared for the frustration. Even engineers that have been in the field for 20 years are still learning something new.

Practice, practice, practice, and you will start to find the patterns that will make you a great developer.

What it’s like on the other side

After working at Walmart Labs for almost 2 years, Jessica is now on the other side of HackerRank for Work assessments. Instead of taking assessments she’s reviewing them. Here’s Jessica’s experience with using HackerRank to find the right candidates for roles at her company. She also has some advice for candidates who are preparing to take a HackerRank assessments in an upcoming interview.

HR: Today you’re on the other side of the HackerRank interview process and are sending out assessments to candidates. What’s that like?

JB: It’s been eye-opening to see how many different ways there are to solve problems. I have to forget how I would solve it, and watch the process someone else goes through.

HR: How has HackerRank helped you and Walmart Labs find the right developers for your company?

JB: HackerRank helps us do some assessment upfront on a candidate’s coding ability. It’s a time-saver for both the candidate and the hiring team.

HR: What advice would you give to someone who is about to take a HackerRank interview assessment?

JB: I would encourage them to get used to how HackerRank works, how to debug if something isn’t working for them as expected, and to consider different test scenarios. Like any coding environment, there are things you may have to get used to before taking an assessment for an interview, so do some practice problems before starting.

HR: What advice would you give to engineering managers that use HackerRank as a part of their hiring process?

JB: HackerRank provides a handy assessment summary, which I usually take a look at before a candidate’s interview. If a candidate struggled, it is a good opportunity to talk about solutions and their thought process. We can follow up to talk about optimizations or what they would do if we changed the constraints.

What's next for Jessica

HR: Are there any other ways HackerRank has helped you move forward in your personal life?

JB: I appreciate HackerRank’s support for initiatives like Women Who Code and Veterans Who Code. It’s important that resources like HackerRank are available to underrepresented people who are interested in making an industry switch.

HR: What’s next in your career journey?

JB: I get up every day excited to get better at what I do. However, besides working to be a better developer, I also want to look behind me and help junior engineers work their way to being better developers as well. I know what it’s like to be nervous and unsure where to start. One of the best ways for a junior engineer to improve is to have a place to ask questions. I hope I can be a mentor to engineers trying to break into the software industry

In addition to creating new software, Jessica is also passionate about helping others switch careers and attain financial independence. Visit her blog Financial Mechanic to learn more about her career change and her journey to financial freedom.

The post How One Self-Taught Developer Transitioned into Tech Using HackerRank Community appeared first on HackerRank Blog.

In honor of Veterans Day, we’re launching Veterans Who Code. This series of interviews shares the stories of 4 veteran developers. Each one has served our country and navigated the ups and downs of reintegrating into the civilian workforce.  Today, these veterans lead projects at Dropbox, Operation Code, Yelo and Vets Who Code.

In addition to speaking with the veterans in Veterans Who Code, HackerRank also had the privilege of interviewing 3 more veteran developers from Vets Who Code. Founded in 2014 by Jerome Hardaway, Vets Who Code teaches veterans computer science courses and prepares them for the civilian workforce. Since their start, Vets Who Code has helped 250 veterans land developer jobs.

All of the veteran developers we interviewed below are Vets Who Code Alumni. Each one shares the obstacles they faced while transitioning to civilian roles in tech, what changes companies can make to better support veterans, what resources veterans should use to start their career, and how Vets Who Code has supported them in their professional growth.

Carla Kroll, Front-End Developer, Former Aerospace Ground Equipment Mechanic 

Carla Kroll spent 6 years in the U.S. Air force as an Aerospace Ground Equipment Mechanic. It was an accident during her service that placed Carla on the road to becoming a developer. After breaking her ankle on the job, Carla spent her time exploring Paintbrush, an image editing program. Her new found love for Paintbrush inspired her to get an Associate's degree in graphic design and then a bachelor’s degree in computer science. After racking up 10 years of web design experience, Carla joined a Vets Who Code cohort in 2017. Here’s what Carla has to say to veterans who want to start coding:

What is one of the hardest challenges you’ve had to overcome specifically as a veteran developer?

I got a late start in this field. The devs around me have been coding since high school, and that wasn’t even an option when I was in school. Trying to play catch up to a group that is much younger and has lived in the developer mindset has been a challenge.

What coding skills do veterans need to secure developer jobs?

HTML, CSS, and Vanilla JavaScript are going to be fundamental. Patience is pivotal, along with good communication skills. I find that a lot of people in this industry try to talk over other’s heads to make themselves feel smarter than the group, but as I grow with code, I realize that not to be true.

What’s the most exciting thing you accomplished through Vets Who Code?

Being part of this community feels like a great accomplishment. Growing in the field and in the organization has been a source of pride for me. I often question my own abilities but VWC is always there to prop me up.

Have you or do you know of any veterans who have used HackerRank as a tool to help them prepare for the civilian workforce?

I believe a few of the members of Vets Who Code have used HackerRank.

If you had one piece of advice for veteran developers looking for their first civilian developer job, what would it be?

Don’t give up. It can be a tough road but keep applying and you’ll find someone to take a chance on you. Also, study! Learning how to interview cannot be underestimated. Its like basic training. The interview is harder than the job most of the time.

Schuster Braun, Web Dev Boot Camp Instructor, Former Linguist and Missions Manager

Like Carla, Schuster Braun’s started his developer journey while serving. As a Missions Manager in the U.S. Navy, Schuster led the training and tasking efforts for more than 35 sailors. He was also in charge of resolving 3 high priority intelligence system issues. After spending 6 years in the Navy, Schuster walked away with 2 new languages and a slew of technical skills under his belt. A couple of months after returning to the civilian world, Schuster joined Vets Who code. Here’s what Schuster has to say veterans who are trying to break into tech:

What is one of the hardest challenges you’ve had to overcome specifically as a veteran developer?

As a veteran developer, my biggest hurdle was finding a community that I could talk to and feel safe expressing myself around. Being a veteran can feel isolating sometimes.

If a veteran wants to become a developer but doesn’t have coding experience, where should they start?

It depends on what they want to build and do with code. There needs to be a goal that they are trying to get to. If they have that I would say try to teach yourself the tools and keep trying to overcome challenges by yourself. If you need help, find communities to reach out to and ask questions and stay engaged. 

What’s the most exciting thing you accomplished through Vets Who Code? 

Vets Who Code gave me a road map for how to access the tech community at large and gave me the tools to be able to organize Southern Dev Conference, a regional conference in my city, Augusta Georgia.

Have you or do you know of any veterans who have used HackerRank as a tool to help them prepare for the civilian workforce?

I use HackerRank Youtube videos to help teach me data structures and algorithms.

What advice would you give to companies who have their own military hiring programs?

I recommend that they connect with the Department of Defense (DoD) Transition Assistance Programs and veteran training programs to help give veterans perspective to their goals while they’re going through their education.

Eddie Prislac, Vets Who Code Mentor, Senior Software Engineer, and Former Small Arms Repair Technician

Eddie Prislac served as a small arms repair technician. He spent a total of 10 years in the military, 4 years in the marines and later 6 years in the Army National Guard. In between his serving gap, Eddie took IT project management and programming courses online. Once he finished his time serving, he started to apply for jobs in the civilian market. During his job search, his interviewees were impressed with his technical skills but didn’t see how his military skills applied to civilian jobs. This inspired Eddie to become a mentor at Vets Who Code and help other veterans learn technical skills that civilian employers are looking for. Today, Eddie runs Vets Who Code’s applicant process. Here’s some advice Eddie has for veterans who are applying for developer roles:

What coding skills do veterans need to secure developer jobs? 

In my experience, languages come and go in terms of popularity, so having a good grasp on planning and problem-solving, as well as an understanding of how to apply algorithms and patterns is more important than memorizing code syntax. That being said, JavaScript and React are in high demand right now for web developers, as well as Python and Scala for those looking to go into machine learning

As an organization that prepares and equips veterans to become a developer in the market, have you come across any reasons why companies choose not to hire developers? 

You have to know not only the developer market, but your company’s target market. I used to do a lot of work for political candidates, which is seasonal, so there may not be as much work available outside of the election season. When I did work for the energy sector, development jobs were tightly coupled to how well the oil and gas market was doing. On a more general level, when demand for devs is high, companies tend to focus not only on how skilled a candidate is, but how well they’ll mesh with a team. You can be a coding wunderkind, but if you’re not going to get on well with your senior team members, nobody’s going to want you around.

What’s the most exciting thing you accomplished through Vets Who Code? 

Getting put in charge of our applicant progress, and getting to see how quickly our devs progress from start to finish. These folks come in with a varying level of skill and knowledge, but generally lacking in direction. Our focused, mentored approach kicks their learning process and creativity into overdrive. Occasionally, I see people exit the program putting out work that I’m jealous of (as a 10+ year vet in the industry). It’s awesome to me to see that level of skill and professionalism from our students.

If you had one piece of advice for veteran developers looking for their first civilian developer job, what would it be?

 Emphasize your discipline and teamwork skills, and display a willingness to learn from criticism. Peer review is something all good devs have to do and go through.

Anything else you’d like to share with veterans who are considering becoming developers? 

Keep your military bearing, but remember you’re not in the military anymore. Despite the applicability of your military skills in the civilian workforce, it’s a whole different animal. Try to keep swearing in check and guard your tongue, and be tactful, as your co-workers will not be as thick-skinned as when you were in, and may take offense even if none was intended. Lastly, learn how your military training can be applied to working as a developer… you’d be surprised just how many seemingly unrelated skills will actually benefit your work as a dev.

If you’re interested in learning more about Vets Who Code and how you can get involved and help veterans start their developer journey, visit their website here.

The post How Vets Who Code Helps Veterans Start Their Developer Journey appeared first on HackerRank Blog.

This is part 1 of a 3-part series based on the conversation HackerRank’s CEO and co-founder, Vivek Ravisankar had with Atlassian’s Head of Platform, Mike Tria, on engineering management. 

This post discusses how to know if engineering management is for you and how to build key engineering management skills (through improv comedy and other methods).

---

HackerRank’s CEO and co-founder Vivek Ravisankar and Atlassian’s Head of Platform Mike Tria are both developers who transitioned into managerial roles and have gone on to hire many engineering managers. Drawing from their experiences, they break down why engineering management isn’t and shouldn’t be for all developers, and also share what skills an engineering manager needs in order to be successful. 

How to know if engineering management is for you

It’s important to recognize that engineering management is not the only way developers can advance their careers. At Atlassian, there are two separate career ladders mapped out for developers: one leads to becoming an engineering manager and the other to becoming a principal developer. 

“We really wanted to treat these as two separate career tracks, both equivalent to another,” says Mike. “We actually mapped our career ladders to have the same exact levels for managers and developers. It works that way from a compensation perspective, to the nature of how the actual promotion up those ladders works. And so, the move from a senior engineer to a manager is seen as a lateral move within Atlassian.” 

Ways to Assess if Engineering Management is the Right Fit

If you're unsure whether you should become an engineering manager, use some of these methods to see whether engineering management is for you.

Approach your manager and see if they’re open to assigning you smaller managerial tasks so that you can experience what the role of a manager is like. You can also build relationships with engineering managers and see if you can find a great mentor who can help you gain a better understanding of the role. Last, if your employer provides manager apprenticeships or rotational programs, take advantage of those offerings. 

If after these experiences you realize that management isn’t for you and you prefer being an individual contributor, Mike believes some management experience is still valuable. 

“I've actually found some of the best developers we have are people that tried management and went back, so they now have an understanding and respect for the work that goes into it,” says Mike. 

The 5 core engineering management skills

If you’ve had a taste of what engineering management is like and you realize it’s the right role for you, here are the top 5 engineering management skills that Mike and Vivek agree you need to learn:

1. Have a team-oriented mindset

When moving from being a developer to an engineering manager position, Mike and Vivek believe many developers (themselves included) may initially struggle with moving to a team-oriented mindset. 

Instead of valuing the code you write, as an engineering manager you need to value what your team produces. While personal technical skills are still important, a successful leader is someone who should be invested in the work of the entire team.

2. Become an active listener

As a manager, it can be easy to dominate conversations or implement procedures and policies that have worked for you in the past. However, it’s vital for you as a leader to take the time to listen to your team so you can begin to understand what your team needs and how you can help. Enforcing plans without input from your team will cause you to quickly lose credibility as a leader and lose the trust of your team.  

Mike has a few suggestions for how first-time engineering managers or leaders who are joining new teams can become better listeners. 

“Actually jumping into the trenches a little bit and understanding their work helps. I also like to take on a project in my first 3 months. So, pick a meaty project and get in there and just shadow and ask questions,” says Mike. “At Atlassian, we have this expression where it's like you go from listening to speaking to deciding and I think for that first 60 days it really is a matter of looking and speaking.” 

3. Hone your communication skills 

Another key soft skill that engineering managers need is the ability to clearly communicate, both verbally and in writing. 

“You need to be a really strong communicator which is not something that you usually give weight when you're trying to hire an engineer and suddenly when you're getting promoted to an engineering manager, you’re getting involved in cross-functional team meetings, you need to be able to communicate why we're doing what we're doing,” says Vivek. 

Mike chose an unconventional way to hone his communication skills. He joined an improv comedy group.

“So, I was naturally not a great communicator but it was also very lucky because I did a lot of improv comedy throughout my career,” says Mike. “And so, it just so happened that a lot of the skills around listening and how to be able to stand in front of a whole bunch of people, in front of your team, and talk about something that maybe you just heard about one hour ago and didn't have time to prep a slide deck, that's a skill that I think some of the improv can teach you.”

4. Sharpen your business acumen

Along with being able to clearly articulate the work that you and your team are doing, it’s important to understand and build partnerships with other departments in your company. 

An engineering manager role requires you to work cross-functionally. You need to be able to understand the business and how your work fits in with the work of the company’s other departments such as sales, marketing, and customer success teams. To do this, start by consuming content outside your field of expertise, and have 1:1s with peers outside of engineering.  

5. Don’t let your technical skills get rusty

To ensure that you always have a clear understanding of the work being done by your team and to maintain credibility as an engineering leader, you need to keep your technical skills sharp. Technology is always moving at a rapid pace so the technical skills that were relevant 2 years ago, may not be as relevant to the business now. 

“You have to be up on the tech that your team is using, not that you can get down in the trenches and write critical code but, you know, fix a bug every once in a while, load up Jira or look at a few issues and talk to the team about the backlog...Actually do those things that help maintain your credibility that you can then pass on to your team,” suggests Mike.  

Tying it all together

Engineering management is not the only option that developers have when it comes to progressing their careers and companies need a mix of principal engineers, senior developers, and engineering managers to be successful. At the end of the day, engineering managers should be individuals who are interested in and able to represent a technical team. This requires an in-depth knowledge of the responsibilities an engineering manager has as well as building key soft and technical skills such as communication skills and business acumen.

The post Building Key Engineering Management Skills appeared first on HackerRank Blog.

The post Forget Fancy Chefs and Foosball — What Developers Really Want is Balance and Growth appeared first on HackerRank Blog.

The post Adriana’s Triumphant Return to Programming After 14 Years as a Stay-at-Home-Mom appeared first on HackerRank Blog.

In the past, most companies sourced developers from top universities, like Stanford, MIT, and UC Berkeley. Today, technical recruiters and hiring managers, from big and small companies, are finding promising candidates in unconventional talent pools.

One unconventional talent pool that is churning out skilled developers is top coding bootcamps in the US.

At HackerRank, we help companies find the right developer based on their skills. Not pedigree. Using practical coding challenges, employers can standardize the way they measure skills. This is the evolution toward skills-based hiring. We full-heartedly believe that companies can find and hire candidates from top coding schools in the US who have the same skill set as candidates from a top 10 school.

In 2015, HackerRank partnered with The White House and hosted a nationwide online hackathon, to promote their TechHire Initiative. 'Untraditional' developers from underrepresented cities participated and solved coding challenges.

During the hackathon, we became curious about the skills of developers from top coding bootcamps in the US and wondered: Which bootcamps performed the strongest on HackerRank coding challenges?

How we determined the top coding bootcamps in the US

TechHire, which is powered by the Opportunity@Work nonprofit, targeted developer communities in 9 states. Fifteen bootcamps participated, and The Software Guild stood out with the most participants and strongest developers.

We looked at bootcamps with the most developers who made it to the 70th percentile. Dozens of employers sponsored the event in kind, and we sent them the contact info of the 70th percentile.

Which languages were most popular in the TechHire Hackathon?

It turns out, bootcamps focused on Javascript more so than traditional colleges. Javascript’s popularity has been skyrocketing. Because it's - well - everywhere. It’s used by 94% of all websites.

"Bootcamps have a limited amount of time to teach, and Javascript is a more practical skill in the industry," says Dr. Heraldo Memelli, the lead content curator at HackerRank. "Javascript has increasingly become one of the most sought after skills. It's a tool that new developers can use to build things fast."

The Bottom Line

Strong developers can come from anywhere, from unlikely towns and unlikely backgrounds. It's hard to measure someone's skills based on a certificate of completion. Standardized skill assessment is expanding the talent pool for companies and uncovering untraditional talent.

Untraditional developers may not look as good on paper. But they could have the drive, ability, and skills to do the job. All you have to do is give them the opportunity to prove their skills to you first.

---

If you enjoyed this piece, subscribe here to get notified the next time we have an interesting, new piece on tech hiring.

---

If you liked this piece, you might like:

• Which Universities Have the Best Coders in the World?
• Which Country Would Win the Programming Olympics?
• Which Countries Have the Best Female Developers?

 

The post Top Coding Bootcamps in the US for Growing Developers appeared first on HackerRank Blog.