Building a career in Data Science

New Roles

Starting a new role in data science requires a strategic approach to learning and adaptation. Emily Robinson emphasizes the importance of asking questions and understanding the company's processes without assuming superiority over existing methods 1. She advises new hires to focus on long-term productivity rather than immediate deliverables, suggesting that a supportive company will allow time for ramping up 1. Robinson contrasts the onboarding experiences at startups versus large tech companies, highlighting the differences in structure and resources available 2.

You might have to try to figure out how you even plug into the data source. The data source may have been set up to help push data to the website and not for you to analyze.

--- Emily Robinson

Understanding these dynamics can help new data scientists navigate their roles effectively.

   

Development

Continuous development is crucial for a successful data science career. Emily Robinson suggests engaging in activities like conference speaking, open-source projects, and applying online course learnings to real projects to enhance skills 3. She shares her own experience of meeting her co-author, Jacqueline Knowlis, at a conference, which led to writing their book on building a data science career 4. Robinson stresses the importance of practical application over theoretical learning, as it leads to better skill retention and growth 3.

I think most people learn best or can overestimate their learning just by, like, watching lectures, even doing little problem sets, and learn much better when they have to take that and apply it to a project.

--- Emily Robinson

This approach ensures that data scientists remain relevant and effective in their roles.