- Why should you care about Data Science? - When should you use Data Science? - How can a PM contribute to a Data Science team/product?

1. Be a Top Notch PM Using Data Science
by Farfetch product leader
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5. How to be a Top Notch PM
using Data Science
Sofia Pitta
Product @ FARFETCH

6. $15.7 trillion
game changer
- Source: Study by PricewaterhouseCoopers (PwC)

7. $15.7 trillion
= AI contribution to the global economy in 2030
- Source: Study by PricewaterhouseCoopers (PwC)

8. - Source: 2019 MIT Sloan Management Review and BCG AI survey
'Almost 90% of executives agree that AI
represents an opportunity, but a mere 18% have
tried to use the technology to generate revenue'

9. I. Why should you care about Data Science as a PM
II. When should you use Data Science
III. How can a PM contribute to a Data Science team/product
Agenda
Key topics I'll cover today

14. Disclaimers
I'm someone who has been working as a PM with a Data Science team
(not a Data Scientist myself :) )
All the learnings I'll be sharing with you are based on my own experience
I might use the terms Data Science / AI / Machine Learning interchangeably

15. I. Why should
you care about
Data Science as a PM

16. Let’s first understand what
is a Data Science problem

17. box 1 box 2 box 3
Hi, I'm Sarah and I sell clothes online, which are stored in a warehouse,
and shipped in one of these boxes:

18. box 1 box 2 box 3
Which box should I use
to pack a pair of jeans?
Category + Size of item + Weight
e.g. Jeans + size M + 350 gr

19. box 1 box 2 box 3
Box 2, easy-peasy!
Category + Size of item + Weight
e.g. Jeans + size M + 350 gr
These
characteristics
do not change
over time

20. This a deterministic problem: for this pair of jeans, we will always get the same
recommended box (it behaves in a predictable manner)

21. Portugal France
How to estimate when
the order will arrive to
the customer?
Courier + Regular/Express + Warehouse location +
Customer's location + Hour of the day + Day of the week +
...
e.g. DHL + Express + Lisbon + Paris + 7.06pm + Saturday + ...

22. Portugal France
How to estimate when
the order will arrive to
the customer?
Courier + Regular/Express + Warehouse location +
Customer's location + Hour of the day + Day of the week +
...
e.g. DHL + Express + Lisbon + Paris + 7.06pm + Saturday + ...
These variables do change over time: they will give Sarah
different estimations (outcomes) at different points in time.

23. This a probabilistic problem: the estimated delivery date (output) won't always be
the same.

25. Image Recognition Forecasting
Fraud Detection Recommendation Engine Churn Prediction
Automation Logistics Optimisation Price Optimisation
Recognise faces/products for automation/enhancements Predict the future
Identify suspicious behaviours Make tailored recommendations Prevent customers from leaving
Eliminate manual repetitive tasks Optimise your supply chain Value-based, dynamic pricing
Machine Translation
Translated text in an automated way

26. you don't have to reinvent the wheel.

27. some problems (many!) are solved by Data Science. some are not.

28. II. When should you
use Data Science

29. Not every problem related to data is a Data Science problem.
A good thumb rule is “If a problem can be solved in Excel, you
don’t need a Data Scientist to handle it.”
- Source unknown

30. FRAMEWORK
TO HELP YOU DECIDE

31. Data Science/AI products are automated systems that learn complex patterns from
historical data, and based on those patterns make predictions on unseen data, to make
or recommend business decisions (customer-facing/technical).

33. 01
Your problem is complex.
Data Science/AI products are automated systems that learn complex patterns from
historical data, and based on those patterns make predictions on unseen data, to make
or recommend business decisions (customer-facing/technical).

34. zip code 3
zip code 2
zip code 1

35. $?
new listing

36. There are patterns to learn.
No pattern to learn -
it's random
There is a pattern to learn -
it's a human face
02
Data Science/AI products are automated systems that learn complex patterns from
historical data, and based on those patterns make predictions on unseen data, to make
or recommend business decisions (customer-facing/technical).

37. 03
There’s historical data to learn
from/data is available.
Data Science/AI products are automated systems that learn complex patterns from
historical data, and based on those patterns make predictions on unseen data, to
make or recommend business decisions (customer-facing/technical).

38. 04
Unseen data follows the patterns of
the training data.
2018
2021
dpd
DHL UPS
Data Science/AI products are automated systems that learn complex patterns from
historical data, and based on those patterns make predictions on unseen data, to
make or recommend business decisions (customer-facing/technical).

39. 05
Is at scale.
06
Changes across time.
Additionally, it also makes sense to think about using AI if you also have a problem that:
100 orders or 1,000,000 orders?

40. When to use Data
Science
1. Your problem is
complex.
2. There are
patterns to learn.
3. Historical data is
available.
4. Unseen data
follows the patterns
of the training data.
5. It's at scale. 6. Changes across
time.

41. As a PM, you should understand if it is worth investing in DS
(i.e. if the cost-benefit equation makes sense).

42. III. How can a PM
contribute to a
Data Science
team/product

43. HOW CAN A PM CONTRIBUTE TO A DS TEAM/PRODUCT?
1. Clearly define the problem & the why

44. Who is this for?
What is the underlying pain-point, unmet need or opportunity?
Why should we do it?

45. 2. Measure the value added to the business/customer
HOW CAN A PM CONTRIBUTE TO A DS TEAM/PRODUCT?

46. higher stakes.
higher uncertainty of success.

47. 3. Help your team finding high-impact problems
HOW CAN A PM CONTRIBUTE TO A DS TEAM/PRODUCT?

48. How to find high-impact DS problems?
Where can you automate time-
consuming manual processes?
Where is the friction in
your product?
What are other companies
doing out there?

49. 4. Run a feasibility check
HOW CAN A PM CONTRIBUTE TO A DS TEAM/PRODUCT?

50. Data availability
Do we have the data needed?
If not, can we acquire it?
Functionality Requirements
What are the minimum features needed?
What’s the scale and performance levels we need?
How costly are wrong predictions?
Problem difficulty
Can a human do it?
Have other companies tackled this
problem with DS?
Confidence
Will customers want it?
What value can we deliver in a reasonable amount of time?

51. 5. Start small (before knowing if you should go big)
HOW CAN A PM CONTRIBUTE TO A DS TEAM/PRODUCT?

52. Data availability
Do we have the data needed?
If not, can we acquire it?
Functionality Requirements
What are the minimum features needed?
What’s the scale and performance levels we need?
How costly are wrong predictions?
Problem difficulty
Can a human do it?
Have other companies tackled this
problem with DS?
Confidence
Will customers want it?
What will be viable to build in a reasonable amount of time?
What is your MVP?

53. 6. Identify risks upfront
HOW CAN A PM CONTRIBUTE TO A DS TEAM/PRODUCT?

55. Stages of a DS model (simplified)
=
Research
+
Execution
+
Iterations/
Improvements

56. 7. Make DS products impactful
HOW CAN A PM CONTRIBUTE TO A DS TEAM/PRODUCT?

57. “how can we use returns predictor model to reduce returns
across the customer journey?”

58. - Source: "Early Bird Catches the Worm: Predicting Returns Even Before Purchase in Fashion E-commerce" Paper by Sajan Kedia, Manchit Madan, Sumit Borar

59. - Source: "Early Bird Catches the Worm: Predicting Returns Even Before Purchase in Fashion E-commerce" Paper by Sajan Kedia, Manchit Madan, Sumit Borar

60. - Source: "Early Bird Catches the Worm: Predicting Returns Even Before Purchase in Fashion E-commerce" Paper by Sajan Kedia, Manchit Madan, Sumit Borar

61. Advertising Refunds Checkout

62. 8. Be the advocate & connect the dots
HOW CAN A PM CONTRIBUTE TO A DS TEAM/PRODUCT?

64. Product & Eng -
Front-End
It does take a village to build a (Data Science) product.
You (PM)
Data Science
Data Engineering
Business teams
Product & Eng -
Back Office
Product Analytics
Product
Design

65. 1. Clearly define the problem & the why
2. Measure the value added to the business/customer
5. Start small
6. Identify risks upfront
7. Make DS products impactful
3. Help your team find high-impact problems
HOW CAN A PM CONTRIBUTE TO A DS TEAM/PRODUCT?
8. Be the advocate & connect the dots
4. Run a feasibility check

66. 'What you need to know about product management for AI'
'Everything We Wish We'd Known About Building Data Products'
'Spotify’s Discover Weekly: How machine learning finds your new music'
'Using Machine Learning to Predict Value of Homes On Airbnb'
(Peter Skomoroch and Mike Loukides, 2020)
(DJ Patil and Ruslan Belkin)
(Sophia Ciocca, 2017)
(Robert Chang, Airbnb Engineering & Data Science, 2017)
Recommended articles

67. Thank you
linkedin.com/in/sofiapitta

68. www.productschool.com
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and
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