Data Solutions & Validation Sr. Specialist

Robinhood • Denver, NY

23h • Hybrid

About The Position

Robinhood is building an elite team applying frontier technologies to the world’s biggest financial problems. The Financial Crimes Team works across multiple verticals to protect customers and ensure compliance with Anti-Money Laundering (AML) and Sanctions regulations. This role is focused on leveraging innovative thinking, advanced analytics, and cutting-edge technology to build a best-in-class Financial Crimes Program. As the Senior Specialist, Model Validation & Analytics, you will execute the validation, testing, tuning, and optimization of various transaction monitoring and filtering programs across Financial Crimes. You will partner closely with the AML Surveillance Team, engineering partners, and cross-functional verticals to ensure financial crimes models and systems are accurately implemented, rigorously evaluated, and effectively calibrated to meet regulatory requirements. This role is based in Denver, CO, New York, NY, or Westlake, TX, with in-person attendance expected at least 3 days per week.

Requirements

• Bachelor's Degree or equivalent in a quantitative, technical, or financial field (e.g., Computer Science, Data Analytics, Statistics, or Finance)
• 5+ years of experience across AML, Financial Crimes, or risk management within the financial services or fintech industry, with a focus on model validation, quantitative analysis, or surveillance system management.
• 3+ years of direct experience in the verification, validation, testing, or tuning of AML rule-based systems and/or machine learning models, along with a working knowledge of core Model Risk Management (MRM) principles and governance standards.
• Proficiency in SQL and experience using quantitative tools or programming languages (e.g., Python, R) to query large datasets, perform statistical analyses, and evaluate model performance.
• Excellent verbal and written communication skills with the ability to explain complex technical and quantitative concepts to non-technical partners, coupled with an ability to manage multiple competing priorities in a fast-paced environment.

Responsibilities

• Perform independent, rigorous quantitative and qualitative analyses of rule-based and machine learning financial crimes surveillance models, filtering systems, and detection logic targeting model design, algorithm soundness, data integrity, and change management.
• Act as a technical bridge within a fast-paced fintech environment by leveraging a solid foundation in data engineering, data modeling, and infrastructure. Collaborate seamlessly with software engineers, data engineers, and data scientists to decipher complex logic, evaluate model implementations, test data pipelines, and support the remediation of model issues.
• Execute effective tuning methodologies and statistical analyses to continuously calibrate and enhance surveillance models. Work on strategic initiatives focused on maximizing risk detection capabilities while systematically reducing false positive volumes to ensure an efficient and effective alert review process.
• Conduct comprehensive data validation and logic testing initiatives to establish a strong foundation of data quality. Ensure the completeness, accuracy, and end-to-end reliability of critical data pipelines and inputs feeding compliance models, mitigating systemic risks and safeguarding the integrity of the transaction monitoring ecosystem.
• Serve as a knowledgeable subject matter resource in AML and Model Risk, providing practical domain insights and supporting cross-functional partners in understanding model logic, performance, and risk trade-offs.
• Build strong working partnerships across engineering, product, and compliance teams to ensure technical solutions are conceptually sound, effective, and aligned with risk tolerance.
• Uphold change and issue management policies for covered systems, models, and solutions, ensuring end-to-end traceability and maintaining defensible documentation for internal audit and external regulators.
• Bring hands-on capabilities and experience leveraging AI tools, Large Language Models (LLMs), and automation techniques to enhance daily productivity, elevate deliverable quality, and optimize team workflows.
• Author thorough, concise, and defensible validation reports, change and issues management documentation, and executive summaries that clearly explain model theory, testing logic, performance findings, and actionable recommendations for key stakeholders.

Benefits

• Performance-driven compensation with multipliers for outsized impact
• Bonus programs
• Equity ownership
• 401(k) matching
• 100% paid health insurance for employees with 90% coverage for dependents
• Lifestyle wallet — a highly flexible benefits spending account for wellness, learning, and more
• Employer-paid life & disability insurance
• Fertility benefits
• Mental health benefits
• Company holidays
• Paid time off
• Sick time
• Parental leave
• Catered meals
• Events
• Comfortable workspaces

What This Job Offers

• Job Type: Full-time
• Career Level: Senior