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Product Management for
Data Science
CONFIDENTIAL - FOR OFFICIAL USE ONLY | © 2016-2019 NeuroFlow, Inc.
Product Tank
June 2019

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About me
NeuroFlow Senior Director of Product
HBS 2012
Founder of Eva & Paul Denim
Product experience:

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First, let’s make sure we’re on the same page

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What’s Product Management? Is it this??

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I’d say sometimes it’s this

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… or this

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… but mostly it’s this

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Product management is driving a lean design cycle
PM’s primary role

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So what is Data Science? Is it this??
9

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Data scientists are matchmakers

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… and plumbers

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… and problem solvers

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So we’re focused on the same part of the process
Data’s primary role

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We make a great team!
BillMe

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So how are we different?

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How do PMs define problems for Data Scientists?
1. Data scientists love questions.
○ “Is the standard accepted data true?”
○ “How can users get a personalized experience?”
○ “How can we help make doctors’ lives easier?”
2. Leverage user stories and workflows
○ “Users find that their stress is always high”
○ “As a marketer, I want to see if the personas my agency gave me are true?”
3. Set clear goals:
○ “How will it keep learning over time?”
○ “How can we get to 99% accuracy?”

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Business goals
● Sales support / Sizzle / Upsells
● Cost-cutting
● SEO / marketing / positioning
Model goals
● Accuracy
● Cost impact
● “Lift”
Product goals
● Feature adoption
● Feature engagement/retention
● Referral / WOM
● Customer approval
Business Metrics
● Revenue projections, CRM
● Accounting/finance reporting
● Site traffic, SEO rankings
Model Metrics
● Compare to expectations (e.g. normal?)
● Server costs, scalability
● Compare performance to control
Product Metrics
● DB data
● Analytics, cohort analysis
● Automated referrals, qualitative
● Bug tickets, customer feedback, NPS
First, define the right problems to solve

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1. A data team will want to first try lots of data
○ As a PM, can you help them access more customer data? Labeled?
○ Good time to throw away crazy outcomes
2. Visualize flat first
○ Before building anything interactive, it can be helpful to export a few graphs
3. Make a working prototype
○ Pair with engineers or designers if needed
○ Use invision prototypes if possible
○ Use tools like D3 or BeakerX for interactive visualizations
○ Get customer feedback as early as you can
It can be tempting to go too far

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For example, using charts to visualize accuracy

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So, in summary...
• PMs and Data Scientists make great teammates!
• PMs help Data Scientists find the right problems to solve & get feedback
• Data Scientists bring knowledge of techniques to find the right solutions
• Finding the right problem is a collaborative effort
• Measurement is key
• Take an MVP approach

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Thank you!
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