Interested in this Data Scientist role at Block?
Apply Now →Skills & Technologies
About This Role
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 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 Have
- 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
Technologies We Use and Teach
Block is investing heavily in AI, and our workflows are evolving accordingly. This creates a rare opportunity to work with emerging systems as they take shape, both leveraging them in practice and influencing how they are designed. The team is at the forefront of defining risk governance in an AI\-first environment, with the goal of setting new industry standards.
We're working to build a more inclusive economy where our customers have equal access to opportunity, and we strive to live by these same values in building our workplace. Block is an equal opportunity employer evaluating all employees and job applicants without regard to identity or any legally protected class. We will consider qualified applicants with arrest or conviction records for employment in accordance with state and local laws and "fair chance" ordinances.
We believe in being fair, and are committed to an inclusive interview experience, including providing reasonable accommodations to disabled applicants throughout the recruitment process. We encourage applicants to share any needed accommodations with their recruiter, who will treat these requests as confidentially as possible.
While there is no specific deadline to apply for this role, U.S. roles are typically open for an average of 55 days before being filled by a successful candidate. Please refer to the date listed at the top of this job page for when this role was first posted.
Block takes a market\-based approach to pay, and pay may vary depending on your location. U.S. locations are categorized into one of four zones based on a cost of labor index for that geographic area. The successful candidate’s starting pay will be determined based on job\-related skills, experience, qualifications, work location, and market conditions. These ranges may be modified in the future.
Zone A: $163,600—$225,000 USD
Zone B: $155,400—$213,700 USD
Zone C: $147,300—$202,500 USD
Zone D: $139,000—$191,200 USD
Application Guidelines
Candidates may submit up to 9 active applications within a 60\-day period. Reapplications to the same role are accepted 90 days after a previous application has been reviewed.
Use of AI in Our Hiring Process
We may use automated AI tools to evaluate job applications for efficiency and consistency. These tools comply with local regulations, including bias audits, and we handle all personal data in accordance with state and local privacy laws.
Contact us here with hiring practice or data usage questions.
*Every benefit we offer is designed with one goal: empowering you to do the best work of your career while building the life you want. Remote work, medical insurance, flexible time off, retirement savings plans, and modern family planning are just some of our offering.*
*Block, Inc. (NYSE: XYZ) builds technology to increase access to the global economy. Each of our brands unlocks different aspects of the economy for more people.* *Square* *makes commerce and financial services accessible to sellers.* *Cash App* *is the easy way to spend, send, and store money.* *Afterpay* *is transforming the way customers manage their spending over time.* *TIDAL* *is a music platform that empowers artists to thrive as entrepreneurs.* *Bitkey* *is a simple self\-custody wallet built for bitcoin.* *Proto* *is a suite of bitcoin mining products and services. Together, we’re helping build a financial system that is open to everyone.*
Salary Context
This $139K-$225K range is above the median for Data Scientist roles in our dataset (median: $162K across 211 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 3,824 AI roles we're tracking, Data Scientist positions make up 7% of the market. At Block, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $200,000 based on 697 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($182K) sits 9% below the category median. Disclosed range: $139K to $225K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Block AI Hiring
Block has 5 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span San Francisco Bay Area, CA, US, Seattle, WA, US, New York, NY, US. Compensation range: $225K - $415K.
Location Context
AI roles in New York pay a median of $210,000 across 2,448 tracked positions. That's 5% above the national median.
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (119) are outnumbered by mid-level (1,813) and senior (1,472) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.