Lead Data Scientist (POC Data Science)

Join our team as a Lead Data Scientist, where you will lead our Proof of Concept (PoC) data science team and drive critical projects for enterprise customers and financial institutions. This hands-on role requires a passion for fraud prevention and the ability to build from scratch in high-stakes environments. You will work directly with clients, prototype models, and help build scalable production-ready solutions using fraud analytics and machine learning. Additionally, you will lead and develop a team of data scientists, own POC/POV delivery, define and track performance metrics, and partner cross-functionally with engineering, product, and go-to-market teams.

• Lead and manage a team of data scientists to drive proof-of-concept (PoC) projects for enterprise customers, focusing on fraud prevention.
• Collaborate directly with clients to understand their unique fraud challenges, rapidly prototype proof-of-concept models, and help build scalable production-ready solutions.
• Stay hands-on in the technical work, including building or reviewing machine learning models, conducting in-depth fraud analyses, and shipping production-grade solutions.
• Experience delivering POC/POV engagements with measurable customer outcomes
• 10+ years of experience in fraud/risk data science and analytics with demonstrated impact in fraud, payments, or fintech
• Bias toward action and ownership — you don't wait to be unblocked
• Expertise in BI and dashboarding — Sigma, Tableau, Metabase, or equivalent
• Strong hands-on technical skills — Python and SQL are essential; Spark, Kafka, or feature stores are a plus
• Proven track record with applied ML in fraud or risk — anomaly detection, classification, and graph analytics in production
• 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 communication and stakeholder management — able to translate complex model outputs for both technical and non-technical audiences, including clients and execs
• Background in a high-growth fintech, payments, or financial institution
• Familiarity with real-time decision infrastructure (Flink, Kafka, feature stores)