What Does It Take to Get Your Foot in the Door as a Data Scientist?

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

Whether you’re fresh out of your degree or bootcamp, or looking to transition into a data science role from a different field, the various paths towards landing your first (or second, or third) job cross an ever-shifting terrain. The necessary skill sets continue to evolve, new tools and technologies pop up on a daily basis, and the job market itself has become more competitive in recent years. What’s an aspiring data scientist to do?

Well, a good first step would be to read this week’s highlights, which tackle these perennial questions with up-to-date insights and actionable advice. From finding your footing as a freelancer to ensuring you successfully market your existing knowledge and experience, these articles offer concrete roadmaps grounded in their authors’ own professional journeys. Enjoy your reading!

• The Two Sides of Hiring: Recruiting vs. Interviewing for Data Roles in Diverse Markets
  Having applied to more than 150 positions and reviewed over 500 applications in several countries, Marina Tosic has a unique perspective on the factors that determine data scientists’ job-search success. She breaks them down into useful tips you can apply and tailor to your own specific situation.
• Mathematics I Look for in Data Scientist Interviews
  The amount of math you need as a data professional varies a lot by role, industry, and company—but it’s all but certain you’ll have to come prepared with baseline knowledge in a few key areas. Farzad Nobar, who’s been involved in hiring at Amazon for several years, outlines the topics and approaches you need to be fluent in to demonstrate you have a strong foundation and to set yourself apart from other candidates.

• My Freelance Experience as a Geo Data Scientist on Upwork after 10 Months
  What do you do if you’d like to break into the world of data science freelancing and consulting, but your skill set is “somewhat rare and weird,” as is often the case for practitioners who come from an academic background? Aleksei Rozanov shares pragmatic learnings based on almost a year of work building up his profile and expanding his network on popular freelance platform Upwork.
• What You Need to Know Before Switching to a Data Science Career in 2024
  “Everyone wants to pivot into AI, and yet job ads are all about cloud, dev, and operations. So, what should you do if you’re looking to enter data science in 2024?” Sabrine Bendimerad reflects on the job market’s transformation in recent years, and provides a step-by-step plan for aspiring data scientists who’d like to break into the field but aren’t sure where to start.

Ready to expand your horizons beyond the current job market and its challenges? We hope so—here are some of our best recent articles, on topics ranging from the 2024 Physics Nobel Prize to partitioning algorithms and AI product development.

• What do we mean by “emergent properties” in discussions of LLMs, and how should we assess the validity of such claims? Don’t miss Anna Rogers’ incisive analysis of an increasingly urgent question.
• In the wake of the news that machine learning researchers won the Nobel Prize in physics this year, Tim Lou, PhD presents a lucid explainer on the increasing convergence of the two fields.
• For his new deep dive, Iqbal Rahmadhan walks us through the process of extracting and exploring odds ratios from a logistic regression model using Python and Statsmodels.
• Can clustering techniques offer a solution for detecting coordinated attacks? Trupti Bavalatti’s debut TDS article presents an in-depth look at this novel and promising approach.
• To help you create user-friendly data tables that your colleagues will love, follow along Yu Dong’s detailed outline of five crucial principles, from consistent granularity to complete documentation.
• If you’re interested in the practical aspects of dataflow architecture, you’ll appreciate caleb lee’s thorough walkthrough of his recent project, which focused on creating and scaling a health and fitness data pipeline.
• In the mood for something a bit more theoretical? Tigran Hayrapetyan unpacks the inner workings of a new sequence-partitioning algorithm that can deliver a significant speed improvement thanks to minimal value rearrangements.
• Despite AI’s growing ubiquity, building AI-powered products remains a complex process. Anna Via looks at four major challenges practitioners currently face.

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

What Does It Take to Get Your Foot in the Door as a Data Scientist? was originally published in Towards Data Science on Medium, where people are continuing the conversation by highlighting and responding to this story.