Block

Financial Services

SeniorDataScientist,AI&ModelRisk

$190–275k ~AI est. California, United States Remote Friendly
Market Sentiment
HIGH DEMAND

Neural analysis suggests this role is
optimal for Senior candidates.

The Brief

“Senior Data Scientist, AI & Model Risk at Block. Skills: AI Risk Management, Generative AI, Model Risk Management. Lead AI Risk Assessments for generative AI. Lead AI Risk Assessments for LLM use cases”

Industry & Context.

Financial Services
Problems you'll solve

Drive risk-based decisions

What They're Looking For.

Must Have

5+ years of related experience, Bachelor's degree in quantitative field, 3+ years experience with Master's, PhD without experience, Proficiency in Python, Understanding of generative AI, Understanding of interagency model risk management principles, Knowledge of AI testing methodologies, Familiarity with data privacy principles, Familiarity with data security principles

Nice to Have

Master's degree in AI/ML, Master's degree in Cybersecurity, Master's degree in Data Science, Familiarity with AI governance frameworks, Experience with AI governance tools, Experience with explainability tools, CRISC certification, PRM certification, FRM certification, AI-specific certifications, Prior experience in second-line-of-defense role, Prior experience in internal audit role, Experience developing AI risk governance frameworks

What You'll Do.

Lead AI Risk Assessments for generative AI

Lead AI Risk Assessments for LLM use cases

Embed in enterprise-wide GenAI review process

Coordinate cross-functional SMEs

Manage timelines for reviews

Review AI system design

Assess pre-deployment testing

Design independent testing

Conduct independent testing

Evaluate ongoing monitoring plans

Develop templates for AI Risk Governance

Develop tools for AI Risk Governance

Develop procedures for AI Risk Governance

Monitor evolving regulatory landscape for AI

Incorporate emerging guidance into AI risk governance

Support SFS response to regulatory inquiries

How You'll Work.

Team & Collaboration

Cross-functional SMEs; Legal; Compliance; InfoSec; Data Governance; MRM; ERM; BRC; TPRM; Financial Crimes

Communication Scope

Translate technical AI concepts; Present to regulators

Process & Methodology

Manage multiple concurrent assessments, Prioritize effectively

Full Job Description

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 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 dete

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