Data Science Engineer for Payment Optimization
5 days ago
Stripe is a leading financial infrastructure platform empowering businesses to thrive in the digital economy. With millions of companies relying on us, we're committed to revolutionizing payment processing and risk management.
We're seeking an exceptional Data Science Engineer to join our Payment Intelligence team, optimizing transactions, reducing costs, and mitigating fraud. Your expertise will be instrumental in developing and deploying cutting-edge machine learning models that drive business growth.
Salary and BenefitsThe annual base salary range for this role is $173,100 - $316,800, reflecting the complexity and scope of the position. In addition to a competitive compensation package, you can expect:
- A comprehensive benefits program, including medical, dental, and vision coverage
- Equity participation in the company's success
- A 401(k) plan with company match
Job Description
Your Key Responsibilities- Design and implement machine learning models using popular frameworks (e.g., TensorFlow, PyTorch) to optimize payment processing and risk assessment
- Collaborate with cross-functional teams to develop data-driven solutions, integrating machine learning into our payment infrastructure
- Analyze large datasets to identify trends, patterns, and insights driving business decisions
- Develop and maintain scalable data pipelines to support model training and deployment
- Work closely with engineers, data scientists, and product managers to ensure seamless integration and deployment of machine learning models
- Minimum 3+ years of experience in building and deploying machine learning applications in large-scale distributed systems
- Proven track record of working with complex data sets, designing and training machine learning models, and performing analysis to inform business decisions
- Experience with programming languages such as Python, Java, or Ruby, and familiarization with cloud-based technologies (e.g., AWS, GCP)
- Strong understanding of software engineering principles, data structures, and algorithms
- Advanced degree in a quantitative field (e.g., statistics, physics, computer science)
- Experience working in production environments, ensuring high-quality code and scalability
- Familiarity with DevOps practices and containerization tools (e.g., Docker)
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