Data Scientist, Model Risk Management

Block is one company built from many blocks, all united by the same purpose of economic empowerment. The blocks that form our foundational teams — People, Finance, Counsel, Hardware, Information Security, Platform Infrastructure Engineering, and more — provide support and guidance at the corporate level. They work across business groups and around the globe, spanning time zones and disciplines to develop inclusive People policies, forecast finances, give legal counsel, safeguard systems, nurture new initiatives, and more. Every challenge creates possibilities, and we need different perspectives to see them all. Bring yours to Block.

Team

The Model Risk Management team ensures our machine learning models are safe, reliable, and compliant with regulatory requirements. We focus on building automated validation tools while maintaining high standards for model assessment. Our work helps prevent errors and bias while making sure models are used appropriately across Block. We work closely with teams throughout the company to establish practical governance frameworks, assess model performance, and share best practices. As a core part of the compliance organization, we support all key areas of Block regardless of product or market.

The Role

We're looking for a Data Scientist to help us make our model validation process both more efficient and more thorough. You'll split your time between hands-on model validation work and developing tools to automate these processes. Your experience validating ML models will be key in building practical solutions that help our team work more effectively.

To start, you'll validate machine learning models across our anti-money laundering (AML), Know Your Customer (KYC), and sanctions screening systems. This hands-on work will help you spot patterns and opportunities where automation can help, leading to a greater focus on building and improving our validation frameworks.

We're looking for someone with strong technical skills in both quantitative analysis and software engineering. You should be detail-oriented and familiar with relevant regulations and industry standards. Clear communication and the ability to work both independently and as part of a team are essential.

You Will

• Primary Focus:

  • Build tools that make model validation faster and more consistent
  • Create validation components we can reuse across different projects
  • Develop automated approaches for common tasks like:
  • Checking model performance
  • Running statistical tests
  • Verifying data quality
  • Testing model assumptions
  • Tracking performance changes over time
  • Build testing frameworks that work for different types of models
  • Create tools that help generate clear validation reports
  • Set up systems to continuously monitor model performance

• Immediate Responsibilities:

  • Assess machine learning models using statistical analysis and testing
  • Look for patterns in validation work that could be automated
  • Set up ways to track how models perform over time
  • Keep validation documentation clear and up-to-date
  • Work with other teams to understand their validation needs
  • Build good working relationships while keeping business goals in mind

You Have

Required:

• Advanced degree in Statistics, Mathematics, Physics, Computer Science, or related quantitative field
• 5+ years experience in model validation or risk management, with focus on machine learning models; or 3+ years and a graduate degree
• Strong software engineering practices and experience building maintainable, well-documented code
• Expertise in Python for building robust validation frameworks and automation tools
• Strong understanding of machine learning algorithms and statistical testing methodologies
• Advanced SQL skills for data analysis and validation automation
• Experience with test automation and software testing frameworks
• Strong quantitative skills with the ability to identify patterns in validation processes
• Experience building modular, reusable code and tools
• High ethical standards with a commitment to integrity and professionalism

 Preferred:

• Experience developing internal tools or validation frameworks
• Knowledge of software development best practices (version control, unit testing, CI/CD)
• Experience validating models in regulatory environments (SR 11-7)
• Experience with AML, KYC, fraud detection, or other financial compliance models
• Knowledge of emerging technology validation approaches
• Experience with data visualization tools (e.g., Looker) for monitoring and reporting