The Twists and Turns of Landing a Data Science Job
Whether you’re a freshly minted data scientist or a seasoned practitioner looking for a new opportunity, launching a job search is never a casual undertaking. Throw in a pandemic, worldwide economic uncertainty, and ever-shifting hiring practices, and things look even more daunting that usual.
We’re here to help: TDS authors often write about their own career trajectories and share insights about finding a job in a wide range of data-focused fields. We’ve selected several recent standouts that offer both concrete information and—just as important!—inspiration for those of you ready to embark on a new adventure.
- Understand the process. After being a job searcher herself in 2021, Rashi Desai is now involved in recruiting data analysts at her current workplace. This vantage point gives Rashi a clear sense of the skills companies are looking for, how they screen applicants, and what data professionals can do to succeed in the current hiring landscape.
- Focusing on the fundamentals. As an analyst who has “been around long enough to remember when neon colors and gradients on bar charts were cool,” Josh Berry knows a lot about change. He also learned that core skills matter the most in the long run; while he tailored his advice for people angling for a promotion, it applies just as well for anyone looking for a new role.
- First impressions still matter. You’d think that resumes and cover letters would be a thing of the past by the third decade of the 21st century. Alas, they’re still here—and as Madison Hunter points out, they still matter a lot. Madison’s post is a handy resource if you’d like to refine your data science resume to better highlight your most relevant skills and achievements.
- How to approach interview questions. After years of consulting and hiring, Brian Perron, PhD has mastered the art of interviewing candidates for data-intensive positions. He leans into his expertise to help job seekers understand what the people on the other side of the proverbial table look for, and how they should craft answers that work to their advantage.
- Learn how to bounce back from setbacks. So you had an interview, thought it went really well, and then… you didn’t get the job. What next? Sharan Kumar Ravindran’s recent article is an honest reflection on failure, and a helpful guide for anyone who’d like to leverage an unsuccessful experience into a learning opportunity that helps you improve your interviewing skills.
If you’re not looking for a job but still want to read some great articles (or: you are looking for a job but desperately need to think about something else), you’re in luck. Our reading recommendations this week cover machine learning, chess, and much more:
- Explore the effect of noise and dataset size on Gaussian processes regression in Lydia Nemec’s terrific new post.
- Maxim Khovanskiy’s first TDS article goes deep into the inner workings of AlphaZero, “a revolutionary chess-playing machine the likes of which had never been seen before.”
- For a patient, accessible explainer on regression trees, read Ivo Bernardo’s latest work, which shows how to build them and lays out both their advantages and shortcomings.
- A robust data platform addresses the needs of diverse stakeholders; Rose Day explains how you can provide your internal customers with tools that empower them to make data-informed decisions.
- We hope you didn’t miss our recent Q&A with Google’s Cassie Kozyrkov, but in case you did: here it is, full of sharp insights about choosing the right career path and the value data analysts bring to the table, among other topics.
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Until the next Variable,
TDS Editors
