Recently, I met with Mark, a fellow data scientist, to answer questions about career development and the real-world practice of data science.
🗂️ Topics 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.
Let me know what you think!
