Navigating Data Science Jobs in 2024: Roles, Teams, and Skills
Whether you’re applying to your first internship to running a multidisciplinary team of analysts and engineers, data science careers come with their own specific set of challenges. Some of these might be more exciting than others, and others can be downright tedious—that’s true in any job, of course—but we believe in framing all of these potential drawbacks as opportunities to deepen your knowledge, expand your skill set, and consider new viewpoints.
Our lineup this week brings together a wide range of perspectives and experiences centered around common obstacles in data careers—and proposes effective approaches to overcoming them. Regardless of where you find yourself in your own data science journey, we hope you explore our recommended reads and find insights to bring into your own work.
- The Data ROI Pyramid: A Method for Measuring & Maximizing Your Data Team
While Barr Moses’s actionable roadmap is geared towards data leads and executives, it’s an essential resource for data professionals up and down the corporate hierarchy. After all, everyone can benefit from understanding how their work contributes to the business, and how to demonstrate their impact to a broader, non-technical audience. - Rebuilding the Portfolio that Got Me a Data Scientist Job
A year ago, Matt Chapman wrote the definitive hands-on guide to building a data science portfolio (and went very viral in the process). In his latest post, Matt revisits his approach and proposes several key updates for an even more streamlined workflow and a more customizable end product.
- 5 Habits Spotify Senior Data Scientists Use to Boost Their Productivity
After all the effort that goes into landing a good data job, the real work begins: what can you do to excel at your new position without running the risk of burnout and/or impostor syndrome? Khouloud El Alami presents five concrete ideas you can adapt for your needs, and doesn’t skimp on the nitty-gritty details, either. - 7 Lessons from an ML Internship at Intel
After a long stint as a data scientist in the banking sector, Conor O'Sullivan’s most recent career twist took him to a machine learning internship at tech giant Intel; don’t miss his writeup of his experiences there and the lessons he learned while exploring a new industry and organizational culture.
As usual, our authors have covered a dizzyingly wide spectrum of topics in recent weeks, from AI’s emerging skills to predictive modeling and deep learning. Here’s a sample of standout posts we don’t want you to miss.
- How well can multimodal models perform on visual word puzzles? Yennie Jun tested GPT-4 Vision and Gemini Pro Vision’s abilities in an attempt to measure the level of creative process present in their model generations.
- Physics-informed neural networks may sound like a lofty, theory-heavy concept, but as Shuai Guo shows in a comprehensive overview, their real-world applications are many—and increasing at a healthy clip.
- In an accessible, well-illustrated explainer, Shreya Rao continues to explore fundamental deep learning topics, this time unpacking the process that allows neural networks to learn.
- For her TDS debut, Nithhyaa Ramamoorthy presents a simple and effective framework to help you build confidence in your data presentations.
- If you were looking for a clear, detailed guide to R-squared, aka the coefficient of determination, don’t miss Roberta Rocca’s one-stop resource, which will dispel any lingering confusion around this ubiquitous metric.
- In the mood for an absorbing, hands-on deep dive? Tim Forster walks us through the process of optimizing nonlinear neural networks in more than one dimension using linear solvers.
Thank you for supporting the work of our authors! If you’re feeling inspired to join their ranks, why not write your first post? We’d love to read it.
Until the next Variable,
TDS Team
Navigating Data Science Jobs in 2024: Roles, Teams, and Skills was originally published in Towards Data Science on Medium, where people are continuing the conversation by highlighting and responding to this story.
Whether you’re applying to your first internship to running a multidisciplinary team of analysts and engineers, data science careers come with their own specific set of challenges. Some of these might be more exciting than others, and others can be downright tedious—that’s true in any job, of course—but we believe in framing all of these potential drawbacks as opportunities to deepen your knowledge, expand your skill set, and consider new viewpoints.
Our lineup this week brings together a wide range of perspectives and experiences centered around common obstacles in data careers—and proposes effective approaches to overcoming them. Regardless of where you find yourself in your own data science journey, we hope you explore our recommended reads and find insights to bring into your own work.
- The Data ROI Pyramid: A Method for Measuring & Maximizing Your Data Team
While Barr Moses’s actionable roadmap is geared towards data leads and executives, it’s an essential resource for data professionals up and down the corporate hierarchy. After all, everyone can benefit from understanding how their work contributes to the business, and how to demonstrate their impact to a broader, non-technical audience. - Rebuilding the Portfolio that Got Me a Data Scientist Job
A year ago, Matt Chapman wrote the definitive hands-on guide to building a data science portfolio (and went very viral in the process). In his latest post, Matt revisits his approach and proposes several key updates for an even more streamlined workflow and a more customizable end product.
- 5 Habits Spotify Senior Data Scientists Use to Boost Their Productivity
After all the effort that goes into landing a good data job, the real work begins: what can you do to excel at your new position without running the risk of burnout and/or impostor syndrome? Khouloud El Alami presents five concrete ideas you can adapt for your needs, and doesn’t skimp on the nitty-gritty details, either. - 7 Lessons from an ML Internship at Intel
After a long stint as a data scientist in the banking sector, Conor O'Sullivan’s most recent career twist took him to a machine learning internship at tech giant Intel; don’t miss his writeup of his experiences there and the lessons he learned while exploring a new industry and organizational culture.
As usual, our authors have covered a dizzyingly wide spectrum of topics in recent weeks, from AI’s emerging skills to predictive modeling and deep learning. Here’s a sample of standout posts we don’t want you to miss.
- How well can multimodal models perform on visual word puzzles? Yennie Jun tested GPT-4 Vision and Gemini Pro Vision’s abilities in an attempt to measure the level of creative process present in their model generations.
- Physics-informed neural networks may sound like a lofty, theory-heavy concept, but as Shuai Guo shows in a comprehensive overview, their real-world applications are many—and increasing at a healthy clip.
- In an accessible, well-illustrated explainer, Shreya Rao continues to explore fundamental deep learning topics, this time unpacking the process that allows neural networks to learn.
- For her TDS debut, Nithhyaa Ramamoorthy presents a simple and effective framework to help you build confidence in your data presentations.
- If you were looking for a clear, detailed guide to R-squared, aka the coefficient of determination, don’t miss Roberta Rocca’s one-stop resource, which will dispel any lingering confusion around this ubiquitous metric.
- In the mood for an absorbing, hands-on deep dive? Tim Forster walks us through the process of optimizing nonlinear neural networks in more than one dimension using linear solvers.
Thank you for supporting the work of our authors! If you’re feeling inspired to join their ranks, why not write your first post? We’d love to read it.
Until the next Variable,
TDS Team
Navigating Data Science Jobs in 2024: Roles, Teams, and Skills was originally published in Towards Data Science on Medium, where people are continuing the conversation by highlighting and responding to this story.
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