This document discusses building data science teams at Instacart. It provides an overview of Instacart's business model and data products, describes what data science entails at the company, and offers advice on organizing data science teams and hiring data scientists. Key points include that data science is driven by product needs and business questions, works closely with engineering, and that the ideal size of a team depends on priorities and resources available. Hiring should emphasize cultural fit and technical skills while ensuring diversity.
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@jeremystan
What is Data Science?
Data Products Decision Science
● Mission driven
● Engineering collaboration
● Question driven
● Leadership collaboration
MVP Product
Market Fit
Usage Data
Machine Learning
A/B Testing
Improved Product
Complication
Data Collection
Analysis
Visualization
Communication
Decision
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Data Product: Personalization
↑ Items
per Order
MVP Product
Market Fit
Usage Data
Machine Learning
A/B Testing
Improved Product
Millions of
purchases
Basket
Purchased
Mission: Increase basket size through personalization
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@jeremystan
Decision Science: Growth
Complication
Data Collection
Analysis
Visualization
Communication
Decision
3 years
200 variables
Time
Cohort
Reorder
● Acquisition tactics
● Product roadmap (A/B test ideas)
● Pricing considerations
● Quality of service focus
● Appeasement strategy
Question: How to improve a declining user cohort reorder rate?
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Commitment CoreData
● Data driven
● Personalized
● Optimized
● Critical
● Novel
● Impactful
● MVP
● Control
● Signal
When?
Beware the HIPPO!
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Standalone IntegratedEmbedded
Data Science driven
Strength: Autonomy
Weakness: Marginalization
Sit: Colocated
Organization
Problem driven
Strength: Flexibility
Weakness: Missing Context
Sit: Split
Mission driven
Strength: Ownership
Weakness: 0 to 1
Sit: With engineers
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@jeremystan
By Size & Priority
1 - 5
Engineers
Data
Products
Decision
Science
You
Embedded
Embedded No IdeaStandalone
Integrated Platform
6-20
Engineers
21-100
Engineers
101+
Engineers
Advice
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Mission Driven Working GroupsIntegrated
● Aligned with products
● Operate independently
● Cross eng team & org
● Single threaded leader
● All skills necessary
● Open code base
How Instacart Organizes
Engineering
ConsumerLogistics
Availability
Fulfillment
Growth
Experience
Orders
1
6
15
Designer
Data Scientist
Engineer
Mobile
ProductAnalyst
Rare
Matrixed
Empowered
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Build an Engine Experience > SellingObjective First
● You don’t “find” talent
● Practice makes perfect
● Amortize investments
● Challenges ~= work
● Experience the culture
● De-risk the decision
● Automate technical
● 360° cultural
● Delay bias (if not avoid)
Hiring Principles
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Hiring Goals
success independent of gender, ethnicity, age, etc.
Consume less than 10% of the team’s time
65% of offers extended should be accepted
Make offers to 80% of great candidates entering funnel
90% of hires should be exceptional
Diversity:
Effort:
Success:
Loss:
Accuracy:
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@jeremystan
Technical Hiring Rubric
Problem Structuring
Objective, assumptions & scope
Technical Rigor
Reliable, readable and flexible
Analytical Rigor
Logically sound & complete
Communication
Clear description of work & sharp Q&A
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@jeremystan
Culture
Constantly pulled in
different directions
Fun, but not very
productive
Feels like we have
superpowers
● Insufficient infrastructure
● Underinvested in BI
● Ambiguous priorities
● Low risk tolerance
● Weak engineering partnership
● No leadership sponsorship
● Misguided focus
● Over-invested
● Data is democratized
● Science is table stakes
● Investing in productivity
● Recognized for impact
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SQL SharingEducation
● SQL for all!
● Blazer at Instacart
● DS Demos
● Lunch & Discuss
● Intro Stat Learning
● Engineer → Machine Learning
Data Democratization
fast.ai