Jobs
Benefits & Perks
•Top Tier compensation with equity
•Flexible PTO policy
•Parental leave program
•Wellness benefits
•Remote work flexibility
•Health, dental, and vision coverage
Required Skills
Airflow
TensorFlow
Python
Who we are
About Stripe
Stripe’s mission is to accelerate global economic and technological development. We offer financial infrastructure and a variety of services to serve the needs of a wide range of users, from startups to enterprises, with global scale and industry-leading reliability and product quality. All financial services businesses face a trade-off between access, which we want to expand, and risk, which we want to minimize. We use machine learning to scalably and intelligently optimize across both.
Artificial Intelligence and Machine learning is an integral part of almost every service at Stripe. It is a key investment area with products and use cases that span merchant and transaction risk, payments optimization, identity, and merchant data analytics and insights. We are also using the latest generative AI technologies (such as LLMs and FMs) to re-imagine product experiences and developing AI Assistants and Agents both for our customers (e.g. Radar Assistant and Sigma Assistant), and also to make Stripes more productive across Support, Marketing, Sales, and Engineering roles within the company.
About the team
We are dedicated to accelerating business value delivery via AI/ML through the Payment Intelligence Suite of products (Radar, Auth Boost, Payments Analytics, Authentication, Disputes) and any other Stripe product that works with these solutions. Our mission is to make Stripe the leader in payments performance via AI/ML, and to allow any other Stripe team looking to balance performance across revenue, cost, and risk the same capabilities so that our shared users have a consistently great experience managing performance on Stripe. We work closely with our partners in the Information org, Risk, RFA, and Money-as-a-Service to build solutions that address our users needs and drive revenue for Stripe.
From a data perspective, Stripe handles over $1.4T in payments volume per year, which is roughly 1.3% of the world’s GDP. We process petabytes of financial data to drive analytics and AI/ML solutions. We use a combination of highly scalable and explainable models such as linear/logistic regression and random forests, along with the latest deep neural networks from transformers to LLMs. Our latest innovations have been around figuring out how best to bring transformers and LLMs to improve existing models and also enable entirely new product ideas that are only made possible by GenAI.
What you’ll do
In this role, you will be a transformative leader with the responsibility of overseeing three critical teams within Pay Intel. You will drive the strategic direction and execution for how Payments, and Stripe more generally, adopts AI, and how our suite of performance products leverage ML/AI to provide a consistent experience that generates revenue for Stripe.
Responsibilities
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Lead the development of our decisioning platform, ensuring that ML/AI decisions are consistent across the lifecycle of a payment regardless of which product makes those decisions.
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Collaborate with the ML Foundations team to develop and deploy Stripe’s Foundation Model to risk, conversion and growth opportunities in Payments.
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Extend our performance analytics, observability, and risk management capabilities across Stripe so that users have a consistently high quality performance experience for cost, revenue, and risk across Stripe.
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Expand our Payments Analytics solution to ensure that users are fully aware of performance opportunities and can take full advantage of our suite of products to automate improvements.
Who you are
We’re looking for someone who meets the minimum requirements to be considered for the role. If you meet these requirements, you are encouraged to apply. The preferred qualifications are a bonus, not a requirement.
Minimum requirements
-
10+ years of experience building and shipping ML models that power AI/ML product features, with a strong emphasis on modern technologies such as DNNs, Transformers, and Foundation Models.
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5+ years of experience managing and developing a team of managers, fostering their growth and ensuring alignment with strategic objectives
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A strong builder mentality, with the ability to define a team's charter and lead the development of complex systems from scratch.
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Proven ability to shepherd large, complex projects and drive transformational change in an organization and with partners that depend on your team’s platform services.
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Deep passion for solving really interesting problems, willingness to experiment, engage with customers directly to understand how well our solutions are working, and to build deep knowledge about performance that drives impact across the company.
Preferred qualifications
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Experience with a large-scale, data-rich product in a domain such as payments, commerce, search, or social media.
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Knowledge of the challenges and opportunities in applying ML to fraud prevention, consumer intelligence, or financial services.
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Experience building platforms that accelerate service adoption outside your organization with little maintainability overhead.
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About Stripe

Stripe
Late StageFinancial infrastructure for the internet
8,000+
Employees
South San Francisco
Headquarters
$50B
Valuation
Reviews
2.3
25 reviews
Work Life Balance
2.1
Compensation
4.2
Culture
1.8
Career
3.0
Management
1.9
25%
Recommend to a Friend
Pros
High compensation and competitive pay
Smart and brilliant coworkers
Challenging and rewarding technical work
Cons
Toxic and broken culture
Poor work-life balance and overworking
Layoffs and job instability
Salary Ranges
1,265 data points
Junior/L3
L2
L3
L4
L5
Mid/L4
Senior/L5
Junior/L3 · Data Scientist
53 reports
$311,019
total / year
Base
$180,447
Stock
$89,802
Bonus
$40,770
$213,896
$474,616
Interview Experience
7 interviews
Difficulty
3.3
/ 5
Offer Rate
57%
Experience
Positive 0%
Neutral 71%
Negative 29%
Interview Process
1
Application Review
2
Recruiter Screen
3
Technical Phone Screen
4
Coding Interview
5
Onsite/Virtual Interview Loop
6
Team Matching
7
Offer
Common Questions
Coding/Algorithm
System Design
Behavioral/STAR
Technical Knowledge
Leadership/Management
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