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

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

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

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

Preferred Qualifications

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