Feeling inspired to write your first TDS post? We are always open to contributions from new authors..
The process and requirements for landing your first job in data science or machine learning have changed considerably over the past few years. So has the definition of excelling at your current role. We can attribute this to several 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.
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 may not be able to turn the tide on their own, but they can can take steps to become more professionally resilient and protect your career path from shocks.
The articles we selected for you this week focus on the core skills you need to develop to become more immune to unpredictable trends and lay out concrete steps you can take to cultivate them. From advice for recent graduates on how to land their first internship to insights on effectively managing data teams, they're aimed at professionals from a wide range of roles and seniority levels. Let's dive deeper.
- How to learn to be a data analyst in 2024
“Every day I receive dozens of LinkedIn messages in my inbox from candidates who are having difficulty landing a job despite having acquired the necessary analytical skills.” Natassha Selvaraj reflects on the changes she’s seen in hiring since starting as a data analyst in 2020 and shares helpful tips for candidates looking to update their approach to stand out and thrive in today’s environment. - Your path to success: how to land an internship in machine learning and data science
Getting your foot in the door is often the hardest 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 Nobrega She has fresh, first-hand experience on how to overcome this initial barrier and shares it in a comprehensive, well-structured guide for those seeking internships. - How to Ask for Feedback as a Data Scientist Individual Contributor
Even if you're already several years into your career, there are always opportunities for growth and to develop stronger communication habits with colleagues across your organization. Jose Parreno offers granular suggestions for individual contributors who want constructive, valuable, and specific feedback, and includes a few dozen sample prompts that you can adapt to your own needs. - Leading data science teams to success
Shifting deadlines, cross-functional teammates, technical complexity… Leading a data science project involves keeping track of numerous moving parts, but as you Hans Christian Ekne He explains that there are a number of techniques a manager can leverage to keep things running smoothly: “The key to addressing these issues lies in understanding the capabilities, weaknesses and strengths of each team member, proper planning and a focus on achieving goals.”
