Director of Data Science & Engineering, Fraud Platform

Zepz•,

7d•Remote

About The Position

This is a high-visibility, high-accountability role reporting to C-level and the board. The role owns both the data strategy and model performance for systems that protect revenue and customer trust. The core mandate is to own the generation and consumption of data across the Identity and Financial Crime domains. This role exists to remove the "tax" caused by the current disconnect between data generation in Identity and data consumption in Financial Crime, which leads to problems being discovered late. The Director will lead a team of 25+ Data Scientists, Machine Learning Engineers, and Analytics Engineers across these domains.

Requirements

Track record of leading a data organization of comparable size (15-25 people) and supporting the professional development of managers and senior individual contributors.
Track record of leading mixed technical teams (Data Scientists, ML Engineers, Analytics Engineers) with genuine credibility across all crafts.
Demonstrable ownership of data quality at scale, including contracts, observability, lineage, and semantic consistency, with evidence of downstream impact.
Fluency with modern lakehouse and analytics engineering practices (e.g., Databricks, dbt-style transformation, feature store concepts, real-time feature serving, 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 directly, demonstrated through influencing upstream teams.
At least 8+ years of technical experience in a hybrid individual contributor/management role.
Ability to attract and retain top talent and lead teams with diverse skill sets.
Comfort working with Engineering and Product leaders and building relationships outside of the Product-Engineering spheres.
Exceptional judgment for making good trade-offs in a rapidly scaling environment.
Proficient depth of knowledge around automation and AI and their application in accelerating analysis, improving processes, and enhancing communication.
Practical experience using AI tools in a professional context to increase productivity.
Strong AI literacy, including understanding AI capabilities and limitations, effective prompting, and critical evaluation of AI-generated output.
Awareness of ethical considerations and responsible AI use in the workplace, especially concerning sensitive data, customer experience, and compliance.

Nice To Haves

Prior experience leading FinCrime/Fraud or Identity teams, including understanding of typologies (card fraud, ATO, first-party fraud, synthetic identity, money mule networks), regulatory context, and detection strategy trade-offs.
Understanding of identity verification, KYC, and onboarding data as a data domain.
Experience partnering with Risk, Compliance, and Legal on regulatory obligations.
Experience in incident response leadership for major fraud events, model failures, or data integrity failures.
Experience working at a scaling startup.
Experience with data mesh or domain-oriented data ownership models.
Experience working with stablecoin and cryptocurrencies.
Experience working with AI in a production environment.
Experience working with k8s / gRPC / Spring Boot / Java / Python.
Prior experience using Claude (Anthropic).

Responsibilities

Own the definition, generation, quality, and consumption of data across the Identity and Financial Crime domains, including accountability for data guarantees.
Establish data quality as an engineering discipline with producer/consumer contracts, monitoring, lineage, and clear ownership for critical datasets.
Own the models and analytics for fraud and financial crime detection, including feature engineering, development, deployment, monitoring, and retraining, with accountability for production model performance.
Lead a mixed organization of Data Scientists, Machine Learning Engineers, and Analytics Engineers, understanding the nuances of each craft.
Partner with the Engineering Manager responsible for the fraud detection platform to jointly own the integration between models and the platform.
Collaborate with the central data function, acting as a demanding customer and contributor to core data infrastructure and tooling.
Set standards with Identity engineering teams (who do not report to this role) to influence upstream data production practices.
Create high-performing teams focused on shipping reliable and trustworthy results, managing technical and data debt effectively.
Hire, retain, and develop top talent to ensure the team remains world-class.
Actively participate in resolving model degradations or data issues, including technical discussions and execution support.