Head Of Data Science & Credit Risk

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Head Of Data Science & Credit Risk based in Brazil. As Head of Data Science & Credit Risk, you will lead the strategy behind ML-powered underwriting and credit risk decisioning across multiple fast-growing markets. You will own the full lifecycle of risk models, from development and deployment through monitoring, experimentation, and measurable business impact. The role combines deep technical leadership with responsibility for credit policies, portfolio performance, and responsible lending practices. You will build and mentor a high-performing team of data scientists and risk analysts while remaining actively involved in complex technical challenges. Working closely with Engineering, Product, Finance, and executive stakeholders, you will turn advanced analytics into practical decisions that support sustainable growth. You will help improve approval rates, automate decisioning, identify new customer segments, and strengthen portfolio health through data-driven strategies. This is a greenfield leadership opportunity in a mission-driven fintech environment focused on expanding fairer and more accessible financial services. Accountabilities - ML and model development: Lead the design, testing, deployment, and ongoing improvement of machine learning models for credit decisioning, fraud detection, risk segmentation, customer value, monetization, and marketing attribution. - Underwriting innovation: Develop underwriting algorithms using alternative data sources to strengthen risk assessment while responsibly expanding access to financial services. - Real-time decisioning: Build and scale real-time or near-real-time scoring models across multiple markets and products. - Model governance: Ensure models are interpretable, robust, fair, and reliable, with appropriate monitoring for accuracy, feature stability, performance, and drift.

- MLOps: Establish strong practices for experimentation, model versioning, deployment, monitoring, and production lifecycle management. - Credit risk strategy: Develop and manage credit risk frameworks, policies, approval strategies, risk thresholds, and customer segmentation approaches adapted to individual markets. - Portfolio monitoring: Track portfolio and risk metrics, investigate material changes, and establish early-warning indicators for potential deterioration. - Experimentation: Simulate policy and model changes, lead A/B testing, and use performance data and business KPIs to continuously refine decisioning strategies. - Stress testing and provisioning: Lead stress testing and expected credit loss modeling while partnering with Finance on provisioning and capital allocation. - Market expansion: Develop localized risk models and policies that support expansion into new markets while aligning with applicable regulatory requirements. - Team leadership: Build, lead, coach, and mentor data scientists and risk analysts while remaining hands-on with technical problem-solving and model development. - Strategic planning: Own the data science and credit risk roadmap, aligning priorities with business growth, product development, and market expansion objectives. - Executive communication: Present model performance, portfolio trends, analytical insights, and strategic recommendations clearly to executive leadership and board-level stakeholders. - Cross-functional partnership: Work closely with Engineering, Product, and Finance to translate analytical findings into measurable business outcomes. - External partnerships: Evaluate and establish relationships with alternative data providers and credit bureaus. - Business impact: Improve approval rates while maintaining target default rates and responsible lending standards, reduce time-to-decision, strengthen unit economics, and identify new customer and product opportunities.