Data Scientist, Payments ML Accelerator

Who we are

About Stripe

Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world’s largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone’s reach while doing the most important work of your career.

About the team

The Payments Machine Learning Accelerator is a new team of data scientists and machine learning engineers. The team’s goal is to act as a multiplier that provides access to improved ML techniques and infrastructure, to enable our existing Payments teams to uplevel their own ML practice, and to increase the rate of learnings. The team will directly ideate and build new product features powered by advanced ML, and also serve as an advisor or enabler for various payment related areas like fraud, authorization, cost optimization etc. 

What you'll do

As a data scientist, you will design and prototype advanced ML models. You will have the opportunity to train deep learning models and build feature embeddings, with the aim to produce business impact and raise the bar for tech excellence in the org. You will also have the opportunity to influence the best practices for ML at Stripe. 

Responsibilities

• Build deep learning architectures and feature embeddings for Payment entities such as merchant, issuer, or customer
• Define metrics, generate data insights and conduct experiments to measure business impact
• Design solutions to increase model accuracy, automation, and explainability
• Experiment with advanced ML solutions in the industry and ideate on product applications 
• Collaborate with our machine learning infrastructure team to leverage new infra services for business solutions
• Collaborate with machine learning engineers to ship solutions to production 

Who you are

The ideal candidate has experience in analytics and statistical modeling, values rigor in data and modeling and is passionate about leveraging advanced ML techniques. 

Minimum requirements

• At least 5 years years industry experience doing data science/quantitative modeling
• An advanced degree in a quantitative field (e.g. stats, physics, computer science)
• Proficient in deep learning frameworks (TensorFlow, Pytorch)
• Experience in developing and training deep learning architectures
• Knowledge about how to manipulate data to perform analysis, including querying data, defining metrics, or slicing and dicing data to evaluate a hypothesis

Preferred qualifications

• Experience working with engineering partners to deploy prototypes to production 
• Experience evaluating niche and upcoming ML solutions