Category: Technology

How to Transition Into Data Science—and Within Data Science

Feeling inspired to write your first TDS post? We’re always open to contributions from new authors.

With January just around the corner, we’re about to enter prime career-moves season: that exciting time of the year when many data and machine learning professionals assess their career growth and explore new opportunities, and newcomers to the field plan the next steps towards landing their first job. (It’s also when companies tend to ramp up their hiring after the end-of-year lull.)

All this energy often comes with nontrivial amounts of uncertainty, stress, and the occasional moment of self-doubt. To help you calmly chart your own path and avoid unnecessary second-guessing (of yourself as well as of hiring teams, colleagues, and others), we put together a special edition of the Variable focused on career transitions for both new and current practitioners.

We never miss a chance to celebrate data scientists’ diverse professional and academic backgrounds, and the lineup of articles we’re presenting here reflects that range, too. Whether you’re thinking about a switch to management, are about to jump into your first startup job, or are in the midst of transitioning to data science from a totally different discipline, you’ll find some concrete, experience-based insights to learn from.

• Rewiring My Career: How I Transitioned from Electrical Engineering to Data Engineering
  When your goal is to jump across discipline lines, one of the toughest challenges is learning how to translate existing skills and knowledge and make their value apparent to prospective employers. Loizos Loizou’s debut TDS article offers a detailed account of the author’s successful repositioning from a trained electrical engineer to a data engineer—a change that is far more substantive than the title alone suggests.
• Why STEM Is Important for Any Data Scientist
  A background in the so-called hard sciences doesn’t always map directly onto data-focused job descriptions. As Radmila M. explains, however, the benefits of applying your hard-earned STEM expertise once you’ve moved on to data science are many — and can manifest themselves in unexpected moments when traditional problem-solving approaches fail to produce the desired outcome.
• From Data Scientist to Data Manager: My First 3 Months Leading a Team
  After nearly seven years as a data scientist, Yu Dong took on a new challenge recently and stepped into a management role for the first time. In a thoughtful new post, Yu reflects on “what has changed, what I’ve enjoyed, and what’s been challenging.”

• Are You Sure You Want to Become a Data Science Manager?
  Tackling the management-track conundrum from a different angle, Jose Parreño encourages anyone who’s considering a move away from an individual contributor role to think deeply about their motivations and goals, and to make an informed decision based on a realistic understanding of what becoming a manager actually entails.
• Roadmap to Becoming a Data Scientist, Part 1: Maths
  For aspiring data professionals who are still years away from debating their fit for a manager role, one of the perennial pain points remains the level and amount of math they need to master in order to start their journey on the right foot. Vyacheslav Efimov provides concrete pointers on what you should learn — and how to get started.
• GenAI is Reshaping Data Science Teams
  Setting yourself up for success doesn’t involve a fixed formula; in fields as dynamic as data science and machine learning, the very definition of your role can evolve from one month to the next. This has been especially true in the past couple of years, as generative-AI tools and LLMs have transformed core workflows across industries. Anna Via wrote a focused synthesis of the challenges and opportunities this rapid change presents, and what data teams—and individuals within them—can do to stay nimble and adapt quickly.
• Why a Data Scientist with a PhD Might Kill Your Early-Stage Startup
  It may sound counterintuitive that arriving at a new job with advanced educational credentials can sometimes make you less effective, but that’s precisely the point Claudia Ng drives home in her latest article. While she writes with hiring managers in mind, her insights are particularly valuable for data science PhDs who can adjust their mindset accordingly, and prevent potentially mismatched expectations.
• So It’s Your First Year in AI; Here’s What to Expect
  Congratulations: you’ve landed your dream role at a buzzy AI startup. Now what? Based on his own personal experiences, Michael Zakhary seeks to demystify what the job might entail and to “offer a glimpse into the daily life of an ML engineer — whether you’re working in a small, agile team or part of a larger, more structured organization.”

Thank you for supporting the work of our authors! As we mentioned above, we love publishing articles from new authors, so if you’ve recently written an interesting project walkthrough, tutorial, or theoretical reflection on any of our core topics, don’t hesitate to share it with us.

Until the next Variable,

TDS Team

How to Transition Into Data Science-and Within Data Science was originally published in Towards Data Science on Medium, where people are continuing the conversation by highlighting and responding to this story.