Director of Data Science – Engineering, Fraud Platform
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Job Description
Director leading Zepz’s fraud-platform data strategy, models, and 25+ person technical organization. Protecting cross-border money transfers through Identity and Financial Crime intelligence.
Responsibilities:
- Own the definition, generation, quality, and consumption of data across Identity and Financial Crime domains
- Establish producer/consumer contracts, freshness and integrity monitoring, lineage, dataset ownership, and source-level remediation
- Own fraud and financial-crime detection models and analytics, including feature engineering, development, deployment, monitoring, and retraining
- Lead a mixed organization of 25+ Data Scientists, Machine Learning Engineers, and Analytics Engineers
- Partner with the Engineering Manager responsible for the fraud detection platform
- Collaborate with the central data function on infrastructure and platform tooling
- Set data standards with Identity engineering teams through influence and evidence
- Build high-performing teams while balancing delivery, reliability, and data trust
- Hire, retain, and develop world-class talent
- Review technical work and help execute strategy when models degrade or data breaks
- Establish a measured baseline of data quality across the identity-to-financial-crime path
- Agree ownership models and dataset contracts with central data and Identity engineering leads
- Instrument critical data paths for proactive monitoring
- Assess model performance, degradation, retraining, and concentrated risk
- Assess team capability, flight risks, and hiring needs
- Promote ownership, accountability, and shipping
- Lead incident response for major fraud events, model failures, or data-integrity failures
- Report outcomes to C-level executives and the board
Requirements:
- Track record of leading a data organization of comparable size (roughly 15–25 people)
- Experience supporting the professional development of managers and senior-level individual contributors
- Track record leading mixed technical teams of Data Scientists, Machine Learning Engineers, and Analytics Engineers
- Demonstrable ownership of data quality at scale, including contracts, observability, lineage, and semantic consistency
- Fluency with modern lakehouse and analytics engineering practices, including Databricks or equivalent, dbt-style transformation, feature stores, real-time feature serving, and datasets as products with owners and SLAs
- Experience with production ML in a real-time context, including model monitoring, drift detection, and retraining pipelines
- Proven experience with AWS at scale
- Direct experience leading FinCrime/Fraud or Identity engineering or data science teams
- Ability to achieve outcomes from teams that do not report to you
- At least 8+ years of technical experience in a hybrid IC/management role
- Ability to attract and retain top talent and lead teams with diverse skill sets
- Experience working with Engineering and Product leaders
- Exceptional judgment and ability to make effective tradeoffs while scaling quickly
- Prior experience leading FinCrime/Fraud or Identity teams, including knowledge of card fraud, ATO, first-party fraud, synthetic identity, money mule networks, regulatory context, and customer-experience tradeoffs
- Understanding of identity verification, KYC, and onboarding data as a data domain
- Experience partnering with Risk, Compliance, and Legal on regulatory obligations
- AI literacy, including automation, AI application, prompt effectiveness, critical evaluation of AI output, and responsible AI use
- Practical professional experience using AI tools
- Experience with Claude is nice-to-have, not essential
- Bonus: scaling startup experience, data mesh or domain-oriented data ownership, stablecoins and cryptocurrencies, production AI, k8s, gRPC, Spring Boot, Java, or Python
Benefits:
- Unlimited annual leave
- Great healthcare benefits
- Employee discounts
- Flexible working environment
- Remote work tools
- Team off-sites and connects
- Professional development and support to excel
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