Lead - POC Data Science

What you'll be doing

• Lead and develop a team of IC data scientists — set direction, unblock work, run 1:1s, and grow each person's scope and impact
• Own POC/POV delivery — partner directly with enterprise customers to demonstrate fraud-loss reduction and platform ROI, from first data pull through to stakeholder readout
• Stay hands-on in the technical work — build or review ML models, conduct in-depth fraud analyses, and ship production-grade solutions alongside your team
• Define and track performance metrics — design dashboards and reporting frameworks to measure the effectiveness of risk strategies across clients
• Translate client problems into data solutions — act as a senior point of contact for fraud challenges, turning complex findings into clear recommendations
• Partner cross-functionally with Engineering, Product, and GTM to scope work, influence the roadmap, and ensure fraud solutions and models get instrumented and scaled correctly
• Drive experimentation — support A/B testing to safely validate new strategies before full rollout
• Raise the bar on craft — mentor IC data scientists on modeling rigor, storytelling with data, and client communication

What you'll need

• 10+ years of experience in fraud/risk data science and analytics with demonstrated impact in fraud, payments, or fintech
• 3+ years in a people leadership role (team lead, manager, or tech lead with direct reports) — you've coached data scientists and helped them grow
• Strong hands-on technical skills — Python and SQL are essential; Spark, Kafka, or feature stores are a plus
• Experience delivering POC/POV engagements with measurable customer outcomes
• Proven track record with applied ML in fraud or risk — anomaly detection, classification, and graph analytics in production
• Expertise in BI and dashboarding — Sigma, Tableau, Metabase, or equivalent
• Strong communication and stakeholder management — able to translate complex model outputs for both technical and non-technical audiences, including clients and execs
• Bias toward action and ownership — you don't wait to be unblocked

Nice to have

• Familiarity with real-time decision infrastructure (Flink, Kafka, feature stores)
• Background in a high-growth fintech, payments, or financial institution