How to Start a Data Science Initiative and Grow Your Team
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Fundamentals on how to start a data science initiatives and build the team that will get you there. Not only should you think about why are you are starting a new project, its ROI, its impact to the business' bottomline but the people you'll need to get you there. You must build and grow your team's capabilities and have a strategy for the inevitable turnover of your team. Here are some takeaways on how you can continue to develop and support your projects.
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How to Start a Data Science Initiative and Grow Your Team
- 1. Annie Flippo PyLadies LA January 2020 How to start a successful data science initiative, project and team
- 2. About Me Annie Flippo Data Science Expert | Predictive Analytics | ML & AI | Skier ⛷ Annie Flippo works as the Director of Data Science & Analytics at inMarket, a mobile advertising platform. Her focus is to make data science accessible, intuitive and fun to learn for everyone. She is a tinkerer, a mentor and a robot builder in her free time.
- 3. “ The only Data Science letters I care about are the four Ms – Make Me More Money!”
- 6. Initiative • Brainstorm big ideas • Make new qualitative research • Want to solve problems using data • Improve products, services or processes systematically with data
- 7. Opportunity Cost The cost is what you give up in order to engage in some activity over all other activities
- 8. The ability to produce a better product or service than anyone else Comparative Advantage
- 9. Initiative: Climb The Highest Mountain Mountaineering company: want to show off our touring packages
- 10. Project • Realization of the initiative • Define what you’re trying to solve and less on how • Include stakeholders, executive sponsors, product managers, financing, analysts, engineers, QA, support & more • Deliverables • Timelines • What does success/failure look like?
- 11. Project: Climb Mount Everest What does success look like?
- 12. Success? Is this what we have in mind? Did we spell out the metric for success? Are we done? On Everest
- 13. On Everest Summit Success Climb to the summit was implied In retrospect, everyone can agree this is the measure for success Image reference: summitclimb.com
- 14. Failure Failure 2 A much harder failure Failure 1 Know when to turn around & make a change Image reference: vancouversun.com
- 15. Image reference: Royal Geographic Society/National Geographic Team Team behind the team
- 16. Team Team is divided into different focuses: Data - Where & what is the data? - Generated or purchased - Data Engineers - Cloud / DevOps / Security Teams Analytics / Modeling / Prediction - Data Analysts - Data Scientists Production - Software / Machine Learning Engineers - Operations Team
- 17. Existing member Train & Grow There will be existing members in the group, how to engage them? Transfers from other team Re-train & Grow There will be members on other teams who will want to work on your projects? Hiring new member Process to Hire Well You will need to hire new staff. Hire to fit your needs now but also future needs. Retention Keep What You’ve Got Now, you have a well functioning team, how do you retain your talent? Succession Who Will Come After Who will take your place when you moved on? Build & Grow Team Management Life Cycle Prepare for Anything Have a strong pipeline of colleagues. Start an internship program.