We’re looking for a curious and driven player-coach to lead our Product Analytics team focused on fraud, risk, and decisioning. In this role, you’ll make a meaningful impact by elevating how we measures, detects, and prevents fraud while protecting customer experience and growth. You’ll lead a team of about 5 to 6 analysts partnering closely with Product Engineering, Data Science, and Operations to improve detection systems, optimize models and thresholds, and build the analytics foundation that drives product decisions.
This role sits at the center of that challenge, helping Us measure what’s really happening, detect shifts early, and guide product decisions that protect customers while enabling growth.
If you enjoy solving ambiguous problems in adversarial environments, care deeply about strong measurement and clean analytics, and thrive on building high-performing teams that ship real impact, we’d love to hear from you.
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Leading and developing a high-impact team
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Lead a team of 5 to 6 product analysts working across fraud, risk, and product performance.
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Coach team members on technical excellence (SQL, Python, experimentation, analytical rigor) and product thinking.
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Build a culture of high standards: reproducible analysis, strong documentation, clear communication, and healthy stakeholder partnership.
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Own hiring, onboarding, and career development to scale a strong, durable team.
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Owning fraud and risk analytics strategy
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Define, standardize, and operationalize the right measurement framework for fraud and risk: loss rates, attack rates, precision/recall tradeoffs, false positive costs, time-to-detection, review burden, and customer friction.
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Support the product teams by building and maintaining monitoring to detect fraud pattern shifts, drift, data issues, and emerging abuse vectors.
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Driving model and decisioning optimization with Product Engineering
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Partner with Product Engineering and Data Science on model evaluation, threshold tuning, segmentation strategy, and feature effectiveness.
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Design offline and online validation approaches where standard A/B testing is hard (holdouts, shadow tests, incremental rollouts, pre-post with guardrails).
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Help teams make principled tradeoffs between fraud prevention, conversion, approval rates, latency, operational cost, and customer experience.
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Improving instrumentation, metrics, and analytics foundations
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Ensure event tracking and instrumentation supports key fraud and product questions, and is reliable enough for decision-making.
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Establish metric definitions and guardrails so teams trust dashboards and analysis across product surfaces.
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Create scalable reporting and self-serve tooling so product teams can answer questions quickly without sacrificing rigor.
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Influencing product direction through clear, actionable insights
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Turn ambiguous questions into structured analyses and recommendations that change what teams build.
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Communicate complex results to stakeholders of all backgrounds, from engineers to senior leadership.
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Drive an impact-focused roadmap with clear prioritization, measurable outcomes, and strong follow-through.
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4+ years of experience in product analytics, data science, fraud or risk analytics, or a related quantitative field.
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Proven people leadership experience, including leading and developing teams (player-coach style strongly preferred).
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Expert SQL skills, including complex transformations and strong instincts for correctness, performance, and maintainability.
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Strong Python skills for analysis and prototyping (data manipulation, evaluation, automation).
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Experience evaluating, tuning, and deploying fraud or risk models, with a strong grounding in statistics
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Experience with fraud detection and risk decisioning concepts, such as:
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Precision/recall tradeoffs and cost-sensitive evaluation
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Thresholding and calibration
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Delayed or noisy labels, selection bias, and feedback loops
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Anomaly detection, supervised approaches, and pragmatic rule plus model systems
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Strong product judgment: you can connect fraud decisions to customer experience and business outcomes.
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Strong analytical communication: you can craft narratives that drive alignment and action.
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Familiarity with dashboard design and performance principles, including appropriate visual selection, clear information hierarchy, metric framing, interactivity tradeoffs, and techniques for building performant dashboards on large datasets.
