Staff Applied ML Scientist
Build and maintain fraud detection models and financial risk products through full ML lifecycle, including production code. Requires 6+ years experience (or 8+ with Masters), PhD preferred, strong end-to-end DS/ML skills, 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 via iteration, new data integration, and feature engineering.
- Write production-ready code for real-time decisions.
- Design, perform, and present analyses for data acquisition, product development, risk operations, marketing, sales.
- Collaborate with engineering, risk operations, data acquisitions for data access and quality.
Requirements
- 6+ years relevant experience & PhD or 8+ years & Masters.
- Proven track record solving complex business problems with DS/ML.
- Experience communicating to senior management/stakeholders.
- Strong end-to-end DS development: planning, metrics, buy-in, delivery.
- Practical ML/Stats knowledge; SOTA ML a plus.
- Interest in deep domain expertise (fraud/fintech background bonus).
- Production code and tests experience.
- Detail-oriented for business-changing decisions.
- Startup experience preferred; thrive in fast-paced, varied problems.
Bonus: Familiarity with identity solutions, fintech.
Salary Range
$200,000 - $240,000/year + equity + benefits (health insurance, 401(k) match, flexible PTO, home office stipend).
Skills
Python, Postgres, AWS, EC2, S3, Rds, Redshift, Machine Learning, Fraud Detection, Feature Engineering
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