82. Landing your First Job In Data Science with Jules Malin

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Jules Malin is a data science team leader, career coach, and educator with over a decade of experience in analytics, machine learning, and AI. Based in Silicon Valley, he has led and scaled data science teams composed of analysts, data scientists, and ML engineers.

Jules was Director of Data Science & AI at GoPro for 10 years, overseeing data science teams supporting Product, Marketing, and Finance. He has deep expertise in product and marketing analytics.

Beyond industry, he is an adjunct professor at the University of San Diego, teaching and developing courses for the Masters in Applied Data Science and Applied AI programs. He is also developing executive courses on AI for business leaders.

In his spare time he started Ascend Data Science, which offers digital courses, an online community, and personalized career coaching for analysts, data scientists, and ML/AI engineers. As a data career coach, Jules helps students and data professionals land more job interviews, job offers, and promotions.

Connect with him to learn more.

Here are Five Things We Cover:

1. Portfolios Are More Important Than Resumes

While your resume might get you through the door, your portfolio seals the deal. Jules explains why a complete project—framed around a real-world business problem, with technical documentation and an executive-style summary—can set you apart in a sea of generic GitHub repos.

2. The Best Job Search Strategy: Reverse-Engineer It

Instead of applying to hundreds of jobs and hoping for the best, Jules recommends picking your ideal role, studying the job description, and building your resume around it. This laser-focused approach helps you align with what hiring managers are actually looking for.

3. SQL Is the Secret Weapon Most Applicants Overlook

You might think machine learning is the holy grail—but Jules says SQL is often the deciding factor. It’s foundational to data access, and many programs underemphasize it. Mastering SQL can immediately set you apart in technical screenings.

4. Referrals Matter More Than Ever

Want to skip the resume black hole? Get referred. Jules dives into how internal referrals—especially from someone senior—can 10x your chances of getting an interview. He shares tactical advice on using LinkedIn and alumni networks to make the ask the right way.

5. Your Communication Skills Are Just As Valuable as Your Code

Data science isn’t just about crunching numbers—it’s about storytelling. Can you take a model and explain it to an exec without using buzzwords or jargon? Jules emphasizes the importance of soft skills in interviews and on the job.

Here are Three Actionable Takeaways From This Episode

1. Build a complete, real-world portfolio project: Don’t just upload a Jupyter Notebook to GitHub—frame your project like a case study. Start with a short business brief outlining the problem, follow it with a technical notebook that shows off your coding and analysis skills, and wrap it up with a presentation-style summary that translates insights for non-technical stakeholders. Using lesser-known datasets—like local government or environmental data—can help you avoid blending in with the crowd.
2. Customize your resume for every single role you apply to: That means pulling keywords and skills directly from the job description (think: SQL, product analytics, or A/B testing) and weaving them naturally into your experience and skills sections. This drastically increases your chances of passing applicant tracking systems and catching the attention of hiring managers who are scanning for those exact terms.
3. When reaching out to hiring managers or recruiters on LinkedIn, don’t just say “I’m interested.”: Include your resume in the message, reference the job title you’re applying for, and briefly explain why you’re a strong fit. If you have a portfolio project that aligns with the role, link it. According to Jules, messages like these were the ones he actually opened—and the ones that led to interviews.