Cars.com Inc. is a decision engine for car buyers and a growth engine for our partners. Data Science is the bread and butter of any decision engine and Cars is no different. In this talk, I will discuss how we quantify various parameters of a car and plan to make use of all the data in hand to put predictive models at various stages of a users’ automobile lifecycle. This talk will also cater to students looking to gain knowledge on how data science is utilized at scale while still following certain processes and leading the way for business and product partners.

1. Leveraging Data Science to power e-
commerce.

2. 2
● About Cars.com & it’s business
Agenda
● Data Science at Cars.com Inc
○ Data Science Challenges
○ Identifying duplicate vehicles
○ Building a core DS team
● Questions
● DS is more than ML, much more!
○ An overall perspective
○ Working example

3. 3
About cars.com
A platform/decision engine that helps you sell/buy a car? NO.

4. 4
About cars.com
We are more than that, we manage the entire lifecycle of your car.

5. 5
About cars.com
How do we make money?
Subscriptions
Model
Advertisements
0 Margins in vehicle sales.

6. 6
About cars.com
Value proposition to users?
A few products.
Place your screenshot here
- Relevant Search Results

7. 7
About cars.com
Value proposition to users?
- Relevant Search Results
- Pricing Tool & Car valuation Place your screenshot here

8. 8
About cars.com
Value proposition to users?
- Relevant Search Results
- Pricing Tool Place your screenshot here
- Car & Dealers’ reviews and
ratings

9. 9
About cars.com
Place your screenshot herePlace your screenshot here
- All of these features are
wrappers over predictive
models; aka data products.
- Lean agile methodology
across the company.
- They have underlying data
pipelines, model building and
AB testing platforms; aka
technical products.

10. 10
Data Science Challenges @Carsdotcom
Start off with a user’s first foot print
Diverse & Relevant Cars
Intuitive & Great Product Exp
Understand user while they
research & browse
Enrich experience with data
products
Help them manage
vehicles they own
Help them sell cars. Enrich
experience with data products
Acquire Users via Marketing
Help them manage
their inventory
Consumer/Buyer
IndividualSeller/Dealer
Facebook bidding
SEO/SEM
Improve SIte Relevance
Relevant Site Ads
No duplicate cars
Data widgets and tools,
Compare your car with
existing cars (Dealers)
Pricing badging,
predicting time to sell,
Similar Cars
Vehicle Valuation, Car
Service Predictions

11. 11
Data Science Challenges
Unstructured data; a lot of it
Decode car context using word embeddings
We learn vectors for each feature or attribute of the
vehicle segments and later aggregate those
vectors to form a vehicle vector
Resulting similar vehicles are agnostic of user (not
dealer) context and behaviour

12. 12
Most people when they talk about Machine Learning at an e-commerce company
Predictive Model
This is how I saw Data Science in 2011

13. 13
In 2013
Predictive ModelData Ingestion
Data Analysis
Modeling Strategy
Data
Transformation
Data Validation
Model
Validation/Evaluation
Models as a service

14. 14
Fetch Data
Write
analysis
scripts
Analyse
Results
Verify
Assumptions
Debug
Codes
Try new
approaches.
Clean it
Verify it
Plot it.
Reflection
OOT Analysis
Effect on KPIs
Local & Global
Sampling
MDI
Modeling
Strategizing
Outliers
Removal
Data Analysis
Acquire Right
Data
Ideation
Finalise Target
Variable
Dashboards
Reports
Insights &
Deep Analytics
A/B Testing
Impact Evaluation
Modeling &
Data Products
Data Science
Process
Business Process

15. 15
The whole picture - DS at Cars.com Inc. 2017
Predictive Model
Integrated Job Management, Monitoring, Data/Model Evaluation/Visualization
Logging & Model Tuner
Data Ingestion
Data Analysis
Modeling Strategy
Data
Transformation
Data Validation
Model
Validation/Evaluation
Models as a
service
Real time Data
Batch Processing
Model Config
Model
Database/Back end
User facing

16. “
Inspiration
- Proving media is biased towards certain political organizations and their ideologies
16
As a DS, sooner or later you’d want to gather more skills than just ML/AI
Product and Vision
- Empower users with custom data science models to analyze the way media covers current affairs
- Provide insight into crowd-sourced reviews of media organizations & political figures
- Goto tool for data journalists, political strategists and enthusiasts

17. 17
Prototype in about 300 hours

18. 18
Questions?
Reach out to me at apandey@cars.com; @addhyan_pandey;
addhyanpandey@gmail.com
Growing Cars’s Data Science team. Hiring at various levels.
So, if you’re into cars & data science then cars.com is the place for you.