Robinhood helps retail investors access financial markets through commission-free trading of stocks, ETFs, options, and cryptocurrencies. With it, users can invest with no account minimums, earn rewards through retirement accounts with matching contributions, and access advanced trading tools. Robinhood democratizes investing by making financial markets accessible to everyone, not just wealthy investors.

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Skills

Candidate Availability

Hybrid · Denver; New York; Westlake · Required

Required and preferred rules are kept separate and reflect the wording in the original posting.

About the Role

You will validate, test, tune, and optimize transaction-monitoring and filtering systems across financial crimes. You will analyze rule-based and machine-learning models, evaluate data integrity and model performance, test pipelines, support issue remediation, and document findings and recommendations. You will collaborate with engineering, product, and compliance partners while applying AML and model-risk expertise.

Requirements

Bachelor's degree or equivalent in a quantitative, technical, or financial field
5+ years of experience across AML, financial crimes, or risk management in financial services or fintech
3+ years of experience verifying, validating, testing, or tuning AML rule-based systems or machine-learning models
Knowledge of Model Risk Management principles and governance standards
Proficiency in SQL
Experience with Python or R for querying large datasets, statistical analysis, and model-performance evaluation
Excellent verbal and written communication skills
Ability to manage multiple competing priorities

Responsibilities

Perform quantitative and qualitative analyses of financial-crimes surveillance models, filtering systems, and detection logic
Evaluate model design, algorithm soundness, data integrity, and change management
Collaborate with software engineers, data engineers, and data scientists to evaluate implementations and test data pipelines
Execute tuning methodologies and statistical analyses to calibrate surveillance models
Validate data quality, completeness, accuracy, and end-to-end pipeline reliability
Provide AML and model-risk expertise to cross-functional partners
Build partnerships across engineering, product, and compliance teams
Maintain change and issue management documentation for covered systems and models
Use AI tools, large language models, and automation techniques to improve workflows
Write validation reports, change and issue documentation, and executive summaries

Benefits

Performance-driven compensation with bonus programs
Equity ownership
401(k) matching
100% paid health insurance for employees
90% health insurance coverage for dependents
Lifestyle wallet for wellness and learning
Employer-paid life insurance
Employer-paid disability insurance
Fertility benefits
Mental health benefits
Company holidays
Paid time off
Sick time
Parental leave
Catered meals
Company events