2027 Summer Intern, MS/PhD, Product Data Science
San Francisco, California, United States
- Pay
Multiple pay statements — pay source
Hourly Masters Pay $70—$70 USD The expected hourly rate for this full-time position is listed below. Interns are also eligible to participate in the Company’s generous benefits programs, subject to eligibility requirements.
Hourly PhD Pay $85—$85 USD
Read the full posting- Work setting
- Not stated
- Employment
- Not stated
What you’ll work on
Full postingImprove causal, predictive pricing models with finer feature granularity
Improve pricing models through enhanced time series estimation
Improve how loss from pricing model mispredictions is quantified
From the employer’s posting
Improve causal, predictive pricing models through additional feature granularity, properly regularized
Improve pricing models through enhanced time series estimation
Improve the quantification of loss resulting from pricing model mispredictions
What you’ll bring
All qualificationsCore experience
- Currently enrolled in a PhD program
- Causal inference and/or econometrics expertise
- Predictive modeling experience
- Experience in Python and SQL
Preferred experience
- Experience with causal machine learning approaches such as Double Machine Learning
- Previous experience at ride-hailing or marketplace companies
Qualification wording
Currently enrolled in a PhD program
Causal inference and/or econometrics expertise
Predictive modeling experience
Experience in Python and SQL
Working with Causal Machine Learning approaches (e.g. Double Machine Learning)
Previous experience at ride-hailing or marketplace companies
Education & alternatives
You have: - Currently enrolled in a PhD program - Causal inference and/or econometrics expertise
The expected hourly rate for this full-time position is listed below. Interns are also eligible to participate in the Company’s generous benefits programs, subject to eligibility requirements. Hourly PhD Pay $85—$85 USD
Tools in this posting
- Python
- SQL
Source — Tool mentions in context
- Predictive modeling experience - Experience in Python and SQL We prefer:
Posting record
Report an error- Status in our records
- Live
- First seen by us
- Oct 14, 2026 · 15 days ago
- Recorded sightings
- 100
- Last seen by us
- Oct 30, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
Your next step
- Have your resume and examples of relevant work ready.
- Check the listed location, eligibility and core experience before starting.
Complete your application on careers.withwaymo.com. The employer’s form will show what is required.
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Job description
Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver—The World's Most Experienced Driver™—to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo’s fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states.
Waymo’s Product Data Science team works cross-functionally with Engineering, Product and Operations to help the company make the most informed decisions using data. Our team collaborates on high-impact projects across the company — from driving quality and operational efficiency to market analysis and rider satisfaction scores — we help to safely and efficiently scale the Waymo Driver. We are data-driven, curious, open-minded, and adapt quickly to new information.
Waymo interns partner with leaders in the industry on projects that create impact to the company. We believe learning is a two-way street: applying your knowledge while providing you with opportunities to expand your skill-set. Interns are an important part of our culture and our recruiting pipeline. Join us at Waymo for a fun and rewarding internship!
You will:
- Improve causal, predictive pricing models through additional feature granularity, properly regularized
- Improve pricing models through enhanced time series estimation
- Improve the quantification of loss resulting from pricing model mispredictions
You have:
- Currently enrolled in a PhD program
- Causal inference and/or econometrics expertise
- Predictive modeling experience
- Experience in Python and SQL
We prefer:
- Working with Causal Machine Learning approaches (e.g. Double Machine Learning)
- Previous experience at ride-hailing or marketplace companies
Note: This will be a hybrid onsite internship position. We will accept resumes on a rolling basis until the role is filled. To be in consideration for multiple roles, you will need to apply to each one individually - please apply to the top 3 roles you are interested in.
Source details
Source notes
Source excerptsSelected passages from the saved posting. Check the full description for conditions and exceptions.
- Pay
Hourly Masters Pay $70—$70 USD The expected hourly rate for this full-time position is listed below. Interns are also eligible to participate in the Company’s generous benefits programs, subject to eligibility requirements.
More source context
Hourly PhD Pay $85—$85 USD
- Location & working pattern
San Francisco, California, United States
- Previous experience at ride-hailing or marketplace companies Note: This will be a hybrid onsite internship position. We will accept resumes on a rolling basis until the role is filled. To be in consideration for multiple roles, you will need to apply to each one individually - please apply to the top 3 roles you are interested in. The expected hourly rate for this full-time position is listed below. Interns are also eligible to participate in the Company’s generous benefits programs, subject to eligibility requirements.
- Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.
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