Block
Senior Data Scientist, AI & Model Risk
Remote 30315 Foundational - SFS - Ops role with clear candidate location fit.
PostedRecently added
Eligible countries27 accepted countries
Seniority signalSenior
Work settingRemote
Accepted candidate locations
Role overview
Senior Data Scientist, AI & Model Risk
Requirements and responsibilities
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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 quality, and limitations
- Understanding of interagency model risk management principles, including SR 26-2
- Knowledge of AI testing methodologies, ex. functional testing, bias testing, adversarial testing, and performance monitoring plus familiarity with data privacy and security principles (encryption, access controls, data classification)
- Excellent written and verbal communication and the ability to translate complex technical AI concepts for non-technical stakeholders, senior management, and regulators
- Strong analytical judgment with the ability to manage multiple concurrent assessments, prioritize effectively, and drive risk-based decisions with minimal day-to-day oversight
Nice to haves
- Master's degree in AI/ML, Cybersecurity, Data Science, or related field
- Familiarity with AI governance frameworks (NIST AI RMF, ISO 42001, or equivalent) and the FFIEC IT Examination Handbooks
- Experience with AI governance tools and platforms (model registries, monitoring dashboards, risk scoring systems)
- Experience with explainability tools (SHAP, LIME, attention visualization)
- Certifications: CRISC, PRM, FRM, or AI-specific certifications such as NIST AI RMF practitioner or ISO 42001 Lead Implementer
- Prior experience in a second-line-of-defense or internal audit role at a bank or financial institution
- Experience developing AI risk governance frameworks in environments where prescriptive regulatory guidance does not yet exist
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