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Zepz

Global digital cross-border payments platform

Director of Data Science & Engineering - Fraud Platform

Engineering LeadershipConfirmed live in the last 24 hours
Full-timeRemote in USANo salary listed

About the Job

Requirements

  • A track record of leading a data organization of comparable size, roughly 15–25 people, and supporting the professional development of managers and senior-level individual contributors.
  • A track record of leading mixed technical teams of Data Scientists, Machine Learning Engineers, and Analytics Engineers, with credibility across all three disciplines.
  • 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, including Databricks or an equivalent, dbt-style transformation, feature store concepts, real-time feature serving, and treating datasets as products with owners and service-level agreements.
  • Experience with production machine learning in a real-time context, including model monitoring, drift detection, and retraining pipelines.
  • Proven experience with AWS at scale.
  • Direct experience leading Financial Crime, fraud, or identity engineering or data science teams, rather than only adjacent risk or security teams.
  • Ability to achieve outcomes with teams that do not report to you, including changing producer-team behavior.
  • At least 8 years of technical experience in a hybrid individual-contributor and management role.
  • Ability to attract and retain talent and lead teams with diverse skill sets.
  • Ability to work effectively with Engineering and Product leaders and build relationships outside the Product Engineering sphere.
  • Strong judgment and ability to make tradeoffs while the organization scales quickly.
  • Prior experience leading Financial Crime, fraud, or identity teams, including understanding fraud typologies such as card fraud, account takeover, first-party fraud, synthetic identity, and money mule networks; the regulatory context; and detection-strategy tradeoffs with customer experience.
  • Understanding of identity verification, Know Your Customer, and onboarding data as a data domain.
  • Ability to partner with Risk, Compliance, and Legal on regulatory obligations related to fraud and identity controls.
  • Proficient knowledge of automation and artificial intelligence and their applications to the work, including accelerating analysis and insight generation and improving process and communication design and delivery.
  • Practical experience using artificial intelligence tools professionally to connect information, draft content, build frameworks, or generate insights from data.
  • Strong artificial intelligence literacy, including understanding the capabilities and limitations of AI assistants, effective prompting, and critical evaluation of generated output.
  • Awareness of ethical considerations and responsible artificial intelligence use, particularly regarding sensitive data, customer experience, and compliance.

Responsibilities

  • Own the definition, generation, quality, and consumption of data across the Identity and Financial Crime domains, including what is produced, its meaning and guarantees, and accountability when those guarantees are not met.
  • Establish data quality as an engineering discipline through producer and consumer contracts, freshness and integrity monitoring, lineage, named owners for critical datasets, and a path from detected anomalies to source fixes.
  • Own the models and analytics used to detect fraud and financial crime, including feature engineering, model development, deployment, monitoring, and retraining; own production model performance.
  • Lead a mixed organization of Data Scientists, Machine Learning Engineers, and Analytics Engineers, understanding and supporting all three disciplines and their career paths.
  • Partner with the Engineering Manager who owns the fraud detection platform, jointly owning the seam between model outcomes and platform outcomes.
  • Partner with the central data function by building domain data products and quality practices on its infrastructure and tooling and feeding requirements back to it.
  • Set standards with Identity engineering teams that do not report to the role, using evidence to make the cost of poor data visible and helping upstream teams improve data production.
  • Build high-performing teams that ship without sacrificing reliability or trust in the numbers, using technical and data debt to move faster while making room to build correctly once requirements are clear.
  • Hire, retain, and develop talent as the organization scales and launches in new markets.
  • Contribute directly when models degrade or data breaks by discussing technical solutions, reviewing work in depth, and helping leads execute strategy.
  • Establish an evidence-based baseline of data quality across the identity-to-financial-crime path, measuring failures, detection, ownership, downstream effort, and model degradation.
  • Agree on a written ownership model with the central data team and Identity engineering leads, including dataset ownership, contracts, and responses to broken data.
  • Instrument the critical path so monitoring detects data issues.
  • Assess the team, identify capability gaps, retention risks, and development opportunities, and begin hiring against a plan.
  • Promote a culture of ownership, accountability, and shipping.
  • Lead incident response when major fraud events, model failures, or data integrity failures occur.

Desired Qualifications

  • Experience working at a scaling startup.
  • Practical experience with data mesh or domain-oriented data ownership models.
  • Experience working with stablecoins and cryptocurrencies.
  • Experience working with artificial intelligence in a production environment.
  • Experience working with Kubernetes, gRPC, Spring Boot, Java, or Python.
  • Prior experience using Claude (Anthropic), which is a nice-to-have rather than essential.

About the Company

Zepz

Global digital cross-border payments platform

Zepz powers WorldRemit and Sendwave, two brands that move money across borders by delivering international transfers online rather than through legacy, cash-based systems. Senders use a cashless online service, while recipients choose delivery options such as bank deposit, cash pickup, mobile airtime top-up, or mobile money. The company operates in 50–130 sending countries across thousands of transfer corridors with a remote-first team of 800+ people. Its goal is to make international digital payments fairer, faster, and more flexible for both senders and receivers.

Company size
501-1,000
Company stage
Not listed
Total funding
Not listed
Headquarters
London, United Kingdom
Founded
2021

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