Senior Data Scientist, First Line Risk

Block•Bay Area, CA, United States of America, CA

8h•$168,300 - $297,000•Remote

About The Position

The First Line Risk Data Science team at Block helps our teams understand, measure, and manage the risks of building financial products that serve millions of customers. We turn complex control, customer, and product data into measurement systems that help teams identify emerging risks, evaluate control performance, and close the gaps that matter most. We’re looking for a Senior Data Scientist to build and scale control performance monitoring for new and existing controls. You’ll help develop our control monitoring platform as the central place for teams to understand risk exposure, investigate changes, and prioritize action. You’ll partner closely with risk, compliance, product, and engineering to build automated measurement into product governance, from defining risk guardrails before launch to monitoring risk outcomes and control effectiveness afterward. This role requires analytical rigor, hands-on building, and exceptional product judgment. You'll own control performance monitoring across a portfolio of controls and products, a large, multi-workstream scope, and be accountable for the long-term health and quality of the metrics and monitoring modules you build. You’ll measure success by the decisions your work improves, the material monitoring gaps it closes, and the risk management impact it delivers.

Requirements

A bachelor's degree in statistics, data science, economics, computer science, or a similar quantitative field with 7+ years of relevant experience; a master's degree with 5+ years; or a PhD with 3+ years.
A track record of leading analytical solutions from problem definition through production use and adoption, including multi-person or cross-team efforts
Advanced proficiency in SQL and Python, including experience working with large datasets and building maintainable analytical code, reusable modules, and automated data workflows
Strong statistical and analytical judgment, including experience with cohort analysis, experimentation, time series, or monitoring, and the ability to recognize selection bias, incomplete data, and misleading metrics
Experience translating business, policy, or control objectives into measurement frameworks that help stakeholders assess performance and take action
Strong product instincts for internal tools, including the ability to understand stakeholder needs, prioritize by impact, and evaluate whether a solution improves how teams work and manage risk
Excellent judgment in ambiguous situations, with the ability to make practical tradeoffs, build stakeholder alignment, and execute across teams
Clear written and verbal communication, with the ability to explain complex analytical findings, uncertainty, and recommendations to technical and nontechnical audiences
Experience in financial technology, risk, compliance, or product governance, or demonstrated ability to learn complex domains and apply rigorous measurement to them
Experience using AI tools to accelerate analysis and development, validate conclusions, and automate repeatable analytical work
Strong proficiency in Python and SQL. Comfort writing clean, organized, and testable code and contributing to software applications that implement data science and analytical artifacts in pipelines or front-ends.
Solid understanding of probability and statistics, including A/B test design and evaluation, standard error calculations and statistical inference, and anomaly detection techniques and methods.
Experience with Python visualization packages (e.g. matplotlib / plotly).
Experience working with git or version control systems.
Experience leveraging LLMs and engineering prompts to accelerate development or analyze non-quantitative data.
Experience with data warehousing platforms (e.g., Snowflake, BigQuery).

Responsibilities

Define and maintain actionable control performance metrics and key risk indicators that connect control objectives, risk exposure, and customer outcomes to clear decisions for first line owners and compliance partners
Investigate how controls work in practice, using source data and system behavior to measure coverage, enforcement, exceptions, and outcomes, and identify where instrumentation or evidence is incomplete
Contribute scalable monitoring metrics and modules into our monitoring application, connecting metrics to risks and controls and helping teams identify emerging risks and prioritize the most consequential monitoring gaps
Turn high-value one-off analyses into reusable analytical modules and automated monitoring workflows that scale across controls and products
Apply statistical judgment to define populations, cohorts, baselines, and thresholds, and calibrate monitoring to detect meaningful changes while managing false positives and data quality limitations
Build repeatable solutions for product governance, including pre-launch risk measurement plans, rollout guardrails, and post-launch monitoring of control performance and risk outcomes
Partner with internal users to understand their decisions and workflows, shape a monitoring roadmap, and measure the value of solutions through adoption and improvements in risk management
Lead ambiguous initiatives from problem definition through delivery, aligning stakeholders on the vision, tradeoffs, ownership, and actions that monitoring should support
Communicate findings and evidence limitations clearly to technical partners, control owners, compliance teams, and senior leaders, and contribute reusable methods and technical guidance for other data scientists