Data Solutions & Validation Sr. Specialist

We need to output HTML fragment with sections: About the role? The posting includes "About the team + role" but we should use headings like About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. We need to include sections only if there is real specific content. We have content: About the team + role (intro). Could map to "About the role". Then responsibilities: the "What You’ll Do" bullet list. Then requirements/qualifications: "What You Bring" includes Education, Experience, Domain Expertise, Technical & Analytical Skills, Adaptability & Communication. Could split into Requirements (Education, Experience) and Qualifications (Domain Expertise, Technical & Analytical Skills) and Skills (maybe combine). But we need to follow instruction: Use headings for those categories if content exists. We also have Benefits: "What We Offer" includes list of benefits. Pay: base pay range zones. Schedule: mention in-person attendance expected at least 3 days per week. Also location info. We must not repeat job title as heading. No Job ID. Remove boilerplate like AI Usage Disclosure, EEO, etc. Also remove application instructions like "Click here to learn more..." and "If our mission energizes you..." etc. Also remove "AI Usage Disclosure" and equal opportunity etc. We must preserve all substantive information. So we need to keep the intro paragraph about mission, etc. Keep "About the team + role" content. Keep responsibilities bullet points. Keep requirements/qualifications/skills content. Keep benefits list. Keep pay ranges. Keep schedule (in-person attendance expected at least 3 days per week). Also location info (Denver, CO, New York, NY, or Westlake, TX office(s)). That's part of schedule/location maybe. We should not create sections with filler like "commensurate with experience". Not present. We need to output only HTML fragment, no /, no markdown fences. We'll structure:

About the role

Join us in building the future of finance. Our mission is to democratize finance for all. An estimated $124 trillion of assets will be inherited by younger generations in the next two decades. The largest transfer of wealth in human history. If you’re ready to be at the epicenter of this historic cultural and financial shift, keep reading. About the team + role We are building an elite team, applying frontier technologies to the world’s biggest financial problems. We’re looking for bold thinkers. Sharp problem-solvers. Builders who are wired to make an impact. Robinhood isn’t a place for complacency, it’s where ambitious people do the best work of their careers. We’re a high-performing, fast-moving team with ethics at the center of everything we do. Expectations are high, and so are the rewards. The Financial Crimes Team at Robinhood works across multiple verticals to protect our customers and ensure strict compliance with all relevant Anti-Money Laundering (AML) and Sanctions regulations. As our team of passionate professionals continues to evolve the program, we are intensely 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 play a key role in this mission by executing 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 our financial crimes models and systems are accurately implemented, rigorously evaluated, and effectively calibrated to meet the diverse regulatory requirements of our domestic and international lines of business. This role is based in our Denver, CO, New York, NY, or Westlake, TX office(s), with in-person attendance expected at least 3 days per week. At Robinhood, we believe in the power of in-person work to accelerate progress, spark innovation, and strengthen community. Our office experience is intentional, energizing, and designed to fully support high-performing teams.

Then responsibilities:

Responsibilities

• Model Validation & Review Execution: 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
• Technical Review & Remediation: 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.
• Optimization & Model Tuning: 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.
• Logic & Data Integrity Assurance: 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 our compliance models, mitigating systemic risks and safeguarding the integrity of our transaction monitoring ecosystem.
• Subject Matter Expertise: 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.
• Cross-Functional Collaboration: Build strong working partnerships across engineering, product, and compliance teams to ensure technical solutions are conceptually sound, effective, and aligned with our risk tolerance.
• Change & Issue Management: 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.
• AI Strategy & Tech-Forward Adoption: 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.
• Documentation & Reporting: 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.

Then Requirements/Education/Experience maybe as Requirements heading:

Requirements

• Education: Bachelor's Degree or equivalent in a quantitative, technical, or financial field (e.g., Computer Science, Data Analytics, Statistics, or Finance)
• Experience: 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.

Then Qualifications:

Qualifications

• Domain Expertise: 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.
• Technical & Analytical Skills: 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.
• Adaptability & Communication: 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.

Maybe Skills heading could be combined but we already have Technical & Analytical Skills under Qualifications. Could also have Skills heading separate but we might avoid duplication. The instruction: Use headings for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). So we should include Skills if there is content. We have Technical & Analytical Skills already; we could put that under Skills. But we also have Adaptability & Communication which could be under Skills as well. However we already used Qualifications for Domain Expertise. Might be better to split: Requirements (Education, Experience), Qualifications (Domain Expertise), Skills (Technical & Analytical Skills, Adaptability & Communication). Let's do that. Thus:

Requirements

• Education: Bachelor's Degree or equivalent in a quantitative, technical, or financial field (e.g., Computer Science, Data Analytics, Statistics, or Finance)
• Experience: 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.

Qualifications

• Domain Expertise: 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.

Skills

• Technical & Analytical Skills: 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.
• Adaptability & Communication: 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.

Then Benefits:

Benefits

• Challenging, high-impact work to grow your career.
• Performance-driven compensation with multipliers for outsized impact, bonus programs, equity ownership, and 401(k) matching.
• Best-in-class benefits to fuel your work, including 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, and mental health benefits.
• Time off to recharge including company holidays, paid time off, sick time, parental leave, and more!
• Exceptional office experience with catered meals, events, and comfortable workspaces.

Pay:

Pay

Base pay for the successful applicant will depend on a variety of job-related factors, which may include education, training, experience, location, business needs, or market demands. The expected base pay range for this role is based on the location where the work will be performed and is aligned to one of 3 compensation zones.

• Zone 1 (Menlo Park, CA; New York, NY; Bellevue, WA; Washington, DC) $136,000—$160,000 USD
• Zone 2 (Denver, CO; Westlake, TX; Chicago, IL) $120,000—$141,000 USD
• Zone 3 (Lake Mary, FL; Clearwater, FL; Gainesville, FL) $106,000—$125,000 USD

Schedule:

Schedule

This role is based in our Denver, CO, New York, NY, or Westlake, TX office(s), with in-person attendance expected at least 3 days per week.

We should ensure we didn't include any boilerplate like "Click here to learn more about our Total Rewards..." etc. Also removed AI Usage Disclosure and EEO. Check that we preserved all substantive info: Intro mission, About the team + role content, responsibilities bullet list, requirements, qualifications, skills, benefits, pay ranges, schedule/location. Yes. We must not repeat job title as heading; we didn't. We must not put Job ID/requisition numbers as head