Machine Learning Risk and Fraud Engineer
2 days ago
At S.T.E.P., we're building a better banking experience to help teens and young adults achieve financial independence and knowledge at an earlier age. Our mission is to improve the financial future of the next generation by empowering them to start their financial journey today.
The RoleWe're looking for an experienced Senior Machine Learning Engineer to join our team and build and deploy machine learning models for all things Risk and Fraud. As a key member of our team, you'll be responsible for designing, developing, and deploying ML models to enhance our Risk and Fraud detection systems, protecting both Step and our customers from fraud and financial loss.
- You'll take the lead in the Risk/Fraud area, providing technical direction and helping shape the team's strategy.
- Use SQL to efficiently fetch, transform, and manipulate data, ensuring it's ready for model development.
- Write production-grade code to deliver robust machine learning solutions.
- Apply statistics to guide experiments, determine appropriate sample sizes, and evaluate model performance.
In this role, you'll have the opportunity to work on complex problems and make a real impact on our business. You'll also be part of a fast-growing company with a strong mission and vision that puts people first.
What We're Looking For- 5+ years of experience in Data Science or ML Engineering.
- A graduate degree in statistics, economics, applied mathematics, computer science, physical sciences, or a similar technical field.
- Proficiency in SQL and Python.
- Excellent data analysis skills.
- Experience developing and deploying machine learning models.
- Experience working in financial systems, including lending or risk/fraud (not required).
- Competitive salary based on experience: $150,000 - $200,000 per year.
- Full medical and dental benefits.
- High potential for growth.
- Stock in an early-stage startup.
- Flexible WFH and vacation policy.
- Paid parental leave.
- Employee referral program.
- 401K plan.
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