Director of Data Science – Engineering, Fraud Platform

Zepz

. Own the definition, generation, quality, and consumption of data across Identity and Financial Crime domains .

Posted 9/24/2026full-timeRemote • United StatesLeadWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in leading data organizations, focusing on data quality, fraud detection, and analytics within Identity and Financial Crime domains. Proven ability to build high-performing teams, establish data standards, and collaborate effectively with cross-functional stakeholders.

Highest-signal resume keywords

Data Quality ManagementFraud Detection ModelsTeam LeadershipAWS ExperienceFinCrime/Fraud Domain Knowledge

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data QualityFeature EngineeringModel MonitoringReal-Time ML PipelinesDatabricksDbt-Style TransformationAI ApplicationKYC UnderstandingData LineageSemantic Consistency

Soft Skills

Exceptional JudgmentInfluencing SkillsTalent DevelopmentAccountabilityCollaboration

Tools & Technologies

AWSClaudeGRPCSpring BootK8s

Industry Keywords

Identity VerificationRegulatory ComplianceCard FraudSynthetic IdentityMoney Mule Networks

Tech Stack

Tools & technologies

AWSGRPCJavaKubernetesPythonSpringSpring BootSpringBoot

About the role

Key responsibilities & impact

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

What you’ll need

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

Comp & perks

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