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

The process and requirements for landing your first data science or machine learning job have shifted considerably in recent years. So has the definition of excelling at your existing role. We can attribute this to any number of factors: the rise of LLMs and AI-powered tools, unfavorable economic conditions (and the layoffs and hiring freezes that came in their wake), and the shifting terrain around remote work all come to mind.

Periods of transition and uncertainty can be difficult to navigate—especially if you entered the field expecting a smooth ride in a booming, lucrative industry. But there’s no reason to despair: individual data professionals might not be able to turn the tide on their own, but they can take action to become more professionally resilient and shock-proof their career trajectory.

The articles we selected for you this week focus on the core skills you should develop to become more immune to unpredictable trends—and lay out concrete steps you can take to cultivate them. From advice for recent grads on snagging your first internship to insights on effective management of data teams, they cater to practitioners across a wide range of roles and seniority levels. Let’s dive in.

• How I’d Learn to Be a Data Analyst in 2024
  “Everyday, I get dozens of LinkedIn messages in my inbox from candidates struggling to get jobs despite having acquired the necessary analytical skills.” Natassha Selvaraj reflects on the changes she’s seen in hiring since starting out as a data analyst in 2020, and shares helpful tips for job candidates who would like to update their approach in order to stand out and thrive in the current environment.
• Your Pathway to Success: How You Can Land a Machine Learning and Data Science Internship
  Getting your foot in the door is often the toughest step in any career path—and even more so in a highly competitive job market. Having completed two internships just a couple of years ago, Sara Nóbrega has fresh, firsthand experience on how to overcome this initial barrier, and she shares it in a comprehensive, well-structured guide for internship seekers.
• Asking for Feedback as a Data Scientist Individual Contributor
  Even if you’re several years into your career, there are always opportunities for growth — and for developing stronger communication habits with colleagues up and down your company’s org chart. Jose Parreño offers granular pointers for individual contributors who want to elicit constructive, valuable, and specific feedback, and includes a few dozen sample prompts you can adapt for your own needs.
• Leading Data Science Teams to Success
  Changing deadlines, multidisciplinary teammates, technical complexity… Leading a data science project involves keeping track of numerous moving parts, but as Hans Christian Ekne explains, there are a number of techniques a manager can leverage to keep things chugging along smoothly: “The key to navigating these issues lies in an understanding of individual team members capabilities, weaknesses and strengths, proper planning, and a focus on goal achievement.”

Ready to branch out into some other topics? Here are a few more standout articles from the past couple of weeks:

• If you’ve considered contributing to an open-source project, don’t miss Siavash Yasini’s reflections on his own recent experiences and the practical lessons he’s learned along the way.
• In a detailed, patiently explained guide, Xichu Zhang unpacks the inner workings and underlying math of the Taylor series.
• Writing at the intersection of data science and business strategy, Ian Xiao’s latest article tackles a thorny question: why do so many customer personalization programs fail?
• For a thorough introduction to current debates (and research) around AI’s potential to support people with disabilities and address various accessibility issues, don’t miss Stephanie Kirmer’s overview, which also discusses the tradeoffs in areas like data privacy and cultural erasure.
• Catch up with the latest advances in time-series forecasting — Marco Peixeiro digs into the nitty-gritty details of the Reversible Mixture of KAN (RMoK) model, exploring its architecture and demonstrating its power using Python.
• What does it take to make AI agents “remember”? Based on her own experiments, Sandi Besen reports back on agents’ potential and limitations in handling both simple and complex memory tasks, and outlines effective methods to address some of the main challenges.

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 Build Your Own Roadmap for a Successful Data Science Career was originally published in Towards Data Science on Medium, where people are continuing the conversation by highlighting and responding to this story.