Data Science Career Challenges—and How to Overcome Them

On a very basic level, most work-related challenges come from similar sources, regardless of field or industry: having to navigate professional relationships and communicate with people who might not always be on the same page as you. And you have to do that within the constraints of goals, available resources, and limited time—and on top of everything else you might need to deal with in your life.

If we take a closer look, though, we can see different patterns emerge not just across professions and workplace types, but even within well-defined roles and disciplines. That certainly appears to be the case for data and ML professionals, who despite a very broad range of skills and responsibilities, often have to resolve similar issues.

This week, we’re highlighting recent articles that focus on some of these common data science work and career challenges we see pop up again and again; they’re grounded in the authors’ personal experiences, but offer insights that can likely help a wide swath of our community. Enjoy!

• A Guide To Building a Data Department From Scratch . One of the most common scenarios for data professionals at smaller companies also happens to be one of the toughest to handle: being the first (and only) person working with data. Marie Lefevre shares her own journey of creating a data function from the ground up, as well as learnings and takeaways for others in similar situations.
• Lessons from Teaching SQL to Non-Technical Teams . Democratizing access to data has been a common goal for many data teams in the past few years, but making it a reality is rarely easy. Jordan G. explains how he approaches teaching non-technical colleagues to use SQL, and offers tips for anyone else who’d like to organize an internal training around this topic.

• How I Became a Data Scientist Before I Joined LinkedIn . You need a job to gain experience, yet you need experience to land a job… sounds familiar? This conundrum is by no means unique to data science, but it does play out in specific ways in this profession, and Jimmy Wong ’s account of the path that led him to a data role at LinkedIn is a helpful example (and source of inspiration) for early-career data scientists who aren’t sure about their next move.
• 4 Tips from My Job Search Marathon . “Na?vely, I estimated that I would be landing a dream role in a few months. The reality turned out to be longer than this.” Even under the best of circumstances, job searches are rarely fun—and even less so in an uncertain economic landscape like the one we’ve seen in the past couple of years. Ceren Iyim recently spent several months looking for her next opportunity, and has a number of practical tips for other data professionals in a similar situation.

We published some excellent articles on many other topics in recent weeks, so we hope you carve out some time to explore them:

• For a very thorough introduction to Q-learning and its underlying math , don’t miss Cristian Leo ’s accessible deep dive.
• If you use SQLAlchemy and would like to expand your knowledge of the popular toolkit, Lynn Kwong ’s latest guide focuses on making asynchronous database requests .
• Interested in the current state of robotics? Nikolaus Correll shared a thoughtful overview of the latest advances in humanoid-robot technology and how it intersects with cutting-edge multimodal models.
• In the mood for some hands-on tinkering? Ida Silfverski?ld patiently outlined an end-to-end workflow for deploying ETL Pipelines to ECS with Fargate.
• Not sure how to make an informed decision about the data science education path you choose? Khouloud El Alami ?? has some important lessons to share on that front.
• Anyone interested in grid-based algorithms should devote some time to read Rhys Goldstein ’s fascinating exploration of 3D grid neighborhoods .

Thank you for supporting the work of our authors! If you’re feeling inspired to join their ranks, why not write your first post? We’d love to read it .

Until the next Variable,

TDS Team