Lead Data Scientist, Fraud Modelling

We're seeking a Lead Data Scientist specializing in Fraud and Risk to join our Cape Town Data Science team. In this role, you'll be at the forefront of protecting our affiliate marketing ecosystem by researching, developing, and deploying ML models that detect and prevent fraud across attribution, lead quality, and partner compliance. You'll work on high-impact problems spanning traditional fraud patterns and emerging threats—from attribution manipulation to browser extension abuse—while building production systems that scale. This is an opportunity to combine rigorous analytical work with tangible business impact in a fast-moving, adversarial domain.

Responsibilities

Conduct R&D on fraud detection and risk monitoring across the digital advertising ecosystem; Design, prototype, and validate ML models and rule-based systems for fraud detection, partner risk scoring, compliance monitoring, and trust & safety workflows; Research and apply graph-based fraud detection techniques (community detection, link analysis, behavioral clustering) and explore graph database applications; Stay ahead of emerging fraud patterns through continuous learning and collaboration with Product, Compliance, and Trust & Safety teams; Deploy Fraud and Risk ML models to production; own end-to-end delivery from ETL, feature engineering, model training, deployment, to monitoring; Iterate on live models by adding new features, improving performance (precision/recall/F1), and reducing false positives; Partner with MLOps and Engineering to ensure models are robust, scalable, and production-ready; Perform deep-dive analyses on fraud trends, partner behavior, and risk patterns; Translate analytical findings into actionable recommendations for Product, Marketing, and Finance stakeholders; Build dashboards and reports to communicate model performance, fraud impact, and risk metrics to leadership; Work closely with Product, Engineering, Compliance, and Finance to scope requirements, prioritize work, and align on success metrics; Communicate technical work clearly to non-technical audiences; Contribute to a culture of experimentation, documentation, and knowledge sharing

Requirements

5+ years in data science, ML, or advanced analytics, with at least 2+ years focused on fraud detection, risk modeling, or anomaly detection in production environments; Demonstrated experience building and deploying fraud or risk models (classification, anomaly detection, time-series analysis, graph-based methods); Strong Python and SQL; Proficiency with ML libraries (scikit-learn, XGBoost, LightGBM, or similar); Experience with feature engineering, model evaluation (ROC/AUC, precision-recall, cost-sensitive learning), and handling imbalanced datasets; Familiarity with production ML workflows (versioning, monitoring, A/B testing, model retraining); Strong foundation in statistics and ML; ability to design experiments, validate models, and interpret results with business context; Ability to translate complex technical work into clear insights for stakeholders; experience presenting to cross-functional teams; Bachelor's in a quantitative field (CS, Statistics, Math, Engineering, or similar); Master's/PhD preferred

Nice to have

Experience in affiliate marketing, ad tech, or e-commerce fraud (attribution fraud, click fraud, lead validation, coupon abuse); Familiarity with browser extension detection, fingerprinting, or device/user identity resolution; Experience with graph analytics or network-based fraud detection (community detection, link analysis, behavioral clustering); Knowledge of privacy-preserving ML techniques or working with privacy-constrained data; Experience with real-time or near-real-time scoring and low-latency deployment (e.g., REST APIs, streaming pipelines); Familiarity with GCP tools (BigQuery, Vertex AI, Cloud Run) and/or Databricks/Spark for large-scale data processing; Exposure to rule engines, decision trees, or hybrid rule-ML systems for compliance and risk workflows; Adversarial thinking: understand how fraudsters operate and can anticipate evasion tactics and evolving attack vectors; Pragmatic delivery: balance rigor with speed, prioritizing MVPs and iterative improvement over perfection; Business impact orientation: focus on measurable outcomes and communicate ROI clearly; Comfort with ambiguity: thrive in evolving problem spaces, defining your own roadmap; Collaboration and influence: build trust across teams and drive adoption through enablement, documentation, and clear storytelling

Benefits

Responsible PTO policy; up to 12 fully covered therapy/coaching sessions per year with additional dependent coverage; monthly gym reimbursement; Restricted Stock Units (RSUs) as part of total compensation with 3-year vesting schedule; free Coursera subscription and PXA courses; 26 weeks fully paid parental leave for primary caregiver and 13 weeks fully paid leave for secondary caregiver; technology stipend for home office and monthly internet allowance