Published on February 18, 2014
Simplify the business of life Deploying a Data Sciences Team: The Promise and the Pitfalls Diane Chang, PhD Senior Data Scientist
… or Why I love embedding 2
You’ve hired them, now how do you deploy them? 4
• Centralized team • Resident in the business • Embedded in business 5
Data Science 6
My Intuit experience • 5 years as a data scientist • First 4 years in “centralized team” mode • Last year I was embedded for 10 months … and I loved it! 7
My concerns with centralization • • • • 8 Single point of contact Few direct interactions Single source for context Feel less a part of the team
The first embedding experiment • Big Data for the Little Guy • First volunteer 4 +4 +4 +4 +4 +4 +4 +4 +4 +4 3 months 3 months 4 months The result: A new business with data in its DNA! 9
Being one of the team • Direct communications – No “geek speak” • Hallway conversations • Flash mob • Extra context -> better product Plus: • Maintained connection with data science team 10
Why I love embedding • • • • 11 New data “believers” Significant impact on a new product Learned a lot Made new friends
And what I learned… • It’s easy to get disconnected • Enter with an exit strategy • Demand can be overwhelming • Two homes can feel like no home 12
Hey product team - are you ready? • Well-defined problem with significant business value • Data is available • Committed bandwidth from team • Welcoming – have a buddy 13
Data scientist - do you have what it takes? • • • • • Open to change Value learning Easily develop personal relationships Adaptable Translate technical to business terms ∑ ≈ ∏ 14
Since the first experiment… • We are growing the program – A dozen embedding rotations • We are still experimenting – Pods: a team not an individual • Can play a larger strategic role • Can offer a variety of skills 15
And me? • Doing a rotation back in the center • Improving tools and products that benefit many product teams • Increasing my skills and knowledge 16
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