• Model Development: Prototype, evaluate, and help productionize machine learning models for fraud detection; own their ongoing monitoring and retraining cycles. • Experimentation: Design and run experiments to measure the impact of fraud interventions, balancing customer experience against loss reduction. • Risk Assessment: Size fraud typologies across our product lines to inform prioritisation and investment decisions. • System Maintenance: Build and maintain anomaly detection systems to surface novel fraud vectors before they scale. • Cross-Functional Collaboration: Work closely with fraud operations, engineers, product managers, and data analysts to translate model outputs into real-world mitigations.

• Own the supply side of Protege's data platform • Define the stages, validation gates, and quality checks that data passes through • Own the product decisions around metadata generation • Define what 'catalog-ready' means and build the tooling that enforces it • Work with vertical stakeholders to translate their requirements into platform-level standards

• Design, build, and deploy foundational GTM models, including Customer Lifetime Value (LTV) forecasting, Marketing and Sales Attribution, and Propensity models. • Partner with GTM teams to design and analyze controlled experiments across various channels, including website A/B testing, pricing experiments, and marketing campaign effectiveness. • Execute proactive, complex analytical deep dives to discover latent user behavior and root causes of changes in GTM metrics, translating findings into actionable recommendations. • Support the interpretation of Marketing Mix Modeling (MMM) results to help maximize marketing ROI and assess the feasibility of future in-house modeling. • Define, instrument, and govern a unified Key Performance Indicator (KPI) framework that maps GTM activities to high-level business outcomes. • Translate complex statistical findings and model outputs into compelling business narratives for cross-functional partners. • Work closely with Data Engineering to ensure data quality, reliable instrumentation, and the development of reusable predictive assets like model feature stores. • Provide technical guidance to peers and stakeholders on best practices for data exploration, ML modeling, and causal methodologies.

• Oversee the design, development, and implementation of scalable scoring models • Collaborate with the Credit team to define data science strategies • Guide and mentor junior data scientists • Communicate complex technical concepts to non-technical stakeholders • Establish rigorous testing and validation protocols • Stay on top of industry trends and emerging technologies • Work closely with product, engineering, and business teams to integrate data science outputs