How to Level Up as a Data Scientist
Published:
In this episode, I chat with Mark, a fellow data scientist, to answer questions about career development and the real-world practice of data science.
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Transcripts available on YouTube and Substack.
We discussed:
- The value of a master’s degree in data science
- Mentorship and career coaching
- How data science teams identify work to do
- Communication and collaboration with business stakeholders
- Differences between analytics and data science roles
- Managing large vs. small projects
- Career-defining projects and demonstrating impact
- Measuring business value beyond accuracy
- Lessons learned and what I would do differently in my career
- The importance of communication and asking for help
- Returning to marketing analytics and finding satisfaction in hybrid roles
Key Takeaways
- A master’s program provides structure, credentials, and connections — but isn’t the only path.
- Mentorship and coaching accelerate growth and confidence.
- Great data scientists co-create with the business — not just deliver outputs.
- Communication is a superpower; it amplifies technical work.
- The most valued projects are those that clearly improve business outcomes.
- Career pivots and non-linear paths can become your greatest asset.