Senior Data Scientist, AI & Model Risk

It all started with an idea at Block in 2013. Initially built to take the pain out of peer-to-peer payments, Cash App has gone from a simple product with a single purpose to a dynamic ecosystem, developing unique financial products, including Afterpay/Clearpay, to provide a better way to send, spend, invest, borrow and save to our 50+ million monthly active customers. We want to redefine the world’s relationship with money to make it more relatable, instantly available, and universally accessible.

Today, Cash App has thousands of employees working globally across office and remote locations, with a culture geared toward innovation, collaboration and impact. We’ve been a distributed team since day one, and many of our roles can be done remotely from the countries where Cash App operates. No matter the location, we tailor our experience to ensure our employees are creative, productive, and happy.

The Role As a Senior Data Scientist focusing on AI & Model Risk, you will lead and coordinate AI risk assessments for Generative AI and Large Language Model use cases, applying Model Risk Management principles to ensure safe, compliant, and responsible deployment. This role sits at the intersection of Model Risk Management and AI Governance, with close coordination with Information Security, Compliance, and Legal stakeholders. You will tackle complex and ambiguous challenges, applying sound judgment to select appropriate methodologies and drive risk-based decisions with minimal day-to-day oversight. You will partner with senior stakeholders across Risk, Engineering, Legal, Compliance, and Information Security to drive assessment outcomes and continuously strengthen governance practice.

This role puts you at the center of an innovative team within SFS that’s actively shaping how AI risk management works in practice; Operationalizing principles into processes that enable safe, effective AI deployment. If you’re excited by the challenge of shaping how AI gets deployed safely and responsibly in financial services, this is the place to be.

You Will

• Lead end-to-end AI Risk Assessments for generative AI and LLM use cases across the Bank; Embedding in Block’s enterprise-wide GenAI review process, coordinating cross-functional SMEs (Legal, Compliance, InfoSec, Data Governance, MRM, ERM, BRC, TPRM, Financial Crimes), and managing timelines to ensure reviews are completed within SLA.
• Review AI system design and documentation; Including retrieval sources, assumptions, limitations, fallback plans, guardrail configurations, and change management procedures — across banking use cases such as fraud detection, BSA/AML compliance, credit decisioning, and customer-facing applications, ensuring governance controls are commensurate with each use case’s risk profile.
• Assess pre-deployment testing for adequacy inclusive of output integrity, hallucination detection, boundary and edge case testing, ethical and safety guardrails, bias testing, A/B testing, volume testing, and UAT — designing and conducting independent testing as needed.
• Evaluate ongoing monitoring plans for comprehensiveness - including accuracy, hallucination rates, drift detection, sensitive data controls, reliability metrics, CSAT, acceptable performance ranges, and documented remediation procedures.
• Develop and maintain templates, tools, and procedures to support the effectiveness and scalability of the AI Risk Governance Program.
• Monitor the evolving regulatory landscape for AI in banking — including FFIEC IT Examination Handbook standards, FDIC Financial Institution Letters, interagency statements, and the anticipated RFI on AI model risk management referenced in SR 26-2 — and incorporate emerging guidance into the AI risk governance program; support SFS’s response to regulatory inquiries as needed.

You Have

• A minimum of 5 years of related experience with a Bachelor’s degree in a quantitative field; or 3 years and a Master’s degree; or a PhD without experience; or equivalent work experience in risk management, model risk management, or AI risk management
• Proficiency in Python or similar languages for evaluating AI system behavior, writing test scripts, or analyzing model outputs
• Strong understanding of generative AI architectures; Including LLMs, transformer models, RAG systems, and agentic AI, plus hands-on experience interacting with and critically evaluating these systems, sufficient to assess design decisions, output…