Product Data Lead

is the world’s leading commerce partnership marketing platform, transforming the way businesses grow by enabling them to discover, manage, and scale partnerships across the entire customer journey. From affiliates and influencers to content publishers, brand ambassadors, and customer advocates,  empowers brands to drive trusted, performance-based growth through authentic relationships. Its award‑winning products—
Performance (affiliate),
Creator (influencer), and Advocate (customer referral)—unify every type of partner into one integrated platform. As consumers increasingly rely on recommendations from people and communities they trust,  helps brands show up where it matters most. Today, over 5,000 global brands, including Walmart, Uber, Shopify, Lenovo, L’Oréal, and Fanatics, rely on  to power more than 225,000 partnerships that deliver measurable business results.

• 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 for modeling relationships between users, devices, transactions, and partners to uncover coordinated fraud rings and suspicious network patterns.
• Stay ahead of emerging fraud patterns through continuous learning—monitoring industry trends, reviewing academic literature, exploring data for novel anomalies, and collaborating closely with Product, Compliance, and Trust & Safety teams.
• Deploy Fraud and Risk ML models to production; own the 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 (testing, alerts, drift monitoring, retraining pipelines).

• Perform deep‑dive analyses on fraud trends, partner behavior, and risk patterns to inform model strategy and business decisions.
• 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; present findings and tradeoffs in planning forums and reviews.
• Contribute to a culture of experimentation, documentation, and knowledge sharing within the Data Science team.

• Experience: 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.
• Fraud & risk domain expertise: Demonstrated experience building and deploying fraud or risk models (classification, anomaly detection, time‑series analysis, graph‑based methods).
• Technical skills:
• Strong Python and SQL; proficiency with ML libraries (scikit‑learn, XGBoost, Light
  
  GBM, 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).
• Analytical rigor: Strong foundation in statistics and ML; ability to design experiments, validate models, and interpret results with business context.
• Communication: Ability to translate complex technical work into clear insights for stakeholders; experience presenting to cross‑functional teams.
• Education: Bachelor's in a quantitative field (CS, Statistics, Math, Engineering, or similar);
  Master’s/PhD preferred.

• Experience in affiliate marketing, ad tech, or e‑commerce fraud (attribution fraud, click fraud, lead validation, coupon abuse).
• Experience with graph analytics or network‑based fraud detection…