Senior Applied ML Scientist
Build and maintain fraud detection ML models and financial risk products with end-to-end ownership. Requires 4+ years experience (or 6+ with Masters), advanced degree, strong ML/stats skills, production coding, and domain interest in fraud/identity.
About the job
Responsibilities
- Develop and maintain fraud detection models through full lifecycle: data acquisition, featurization, labeling, training, experimentation, productionalization, monitoring.
- Build foundational modeling for expanding Fraud and Financial Risk products.
- Research new fraud types and develop identity verification products.
- Achieve success through iteration, new data integration, inventive feature engineering.
- Write production-ready code for real-time decisions.
- Design, perform, present analyses for data acquisition, product development, risk ops, marketing, sales.
- Collaborate with engineering, risk ops, data teams for data access and quality.
Requirements
- 4+ years relevant experience & PhD or 6+ years & Masters.
- Proven track record solving complex business problems with DS/ML.
- Experience communicating to senior stakeholders.
- Strong end-to-end DS: planning, metrics, buy-in, delivery.
- Practical ML/Stats knowledge; SOTA ML a plus.
- Interest in fraud/financial domain expertise.
- Production code and tests experience.
- Detail-oriented for business decisions.
- Bonus: identity/fintech background, startup experience.
Salary Range: $200,000 - $240,000/year + equity + benefits.
Skills
Python, Postgres, AWS, EC2, S3, Rds, Redshift, Machine Learning, Fraud Detection, Feature Engineering
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