At a Glance
- Tasks: Lead a dynamic team to enhance data quality and fraud detection strategies.
- Company: Join Zepz, a mission-driven tech company transforming financial access globally.
- Benefits: Enjoy unlimited leave, great healthcare, and employee discounts while working remotely.
- Other info: Be part of a diverse team dedicated to breaking down barriers in finance.
- Why this job: Make a real impact in a fast-paced environment focused on global connections.
- Qualifications: Proven leadership in data science and engineering with a focus on fraud prevention.
The predicted salary is between 58500 - 71500 Β£ per year.
- Description
- Join Zepz: Breaking Down Borders, Together
At Zepz, we're all about breaking down barriers for our customers and our teammates.
Our purpose is deeply personal, we provide a lifeline and deliver opportunities to cross-border communities that need it most through cutting edge finance and tech.
Zepz is the power behind World Remit and Sendwave, driven by the mission to celebrate the incredible impact migrants have, both at home and abroad.
We're not just moving money; we're building a world that truly recognizes and supports global connections.
Who You'll Be Joining We look for mission-driven builders who thrive in a fast-paced environment connected to a true purpose.
Weβre an always developing team of experts that enjoy problem solving and bring clarity to tough challenges.
At Zepz, we are Relentless Learners, always seeking feedback, and
Responsible Owners, treating company resources like our own. We depend on Transparent Communicators who keep the team aligned through open, honest talk.
- Our Core Commitments
- β What We Live By
Our vibrant and truly diverse culture is built on three core commitments, that guide how we work and interact:
- Integrity: We always do the right thing. Itβs simple, but itβs the foundation of everything we build for our customers and each other.
- Outcomes: We work for our customers. Their success and prosperity are the results we focus on delivering.
- Velocity: We are fast! We maintain high energy levels and make smart decisions quickly, keeping us nimble and ahead of the curve.
Perks of Joining Our Team
We believe in empowering colleagues because we genuinely trust our team.
Our culture is founded on this high trust, which naturally encourages the high ownership that drives us to meet our shared high expectations and deliver high performance.
While we have all the tools to work remotely, we value the purpose and benefits of in-person connections so we expect our teams to come to the office on a regular basis, and therefore be within a comfortable commutable distance from our
London office .
Alongside a great office environment, think unlimited annual leave , great healthcare benefits, and employee discounts.
We want you to thrive and focus entirely on making your biggest impact!
In turn, we expect you to bring high ownership and commitment to your work.
This is a place where we value trust and high performance, and we'll provide the environment and support needed for you to excel
About the role
This is a high-visibility, high-accountability role β outcomes are reported to C-level and the board, and the role owns both the data strategy and the model performance behind systems that directly protect revenue and customer trust.
The mandate in one line: own what data is generated and how it is generated and consumed across the Identity and Financial Crime domains.
Almost every consequential decision Zepz makes about a transaction β is this person who they say they are, is this payment safe, is this account compromised β is a decision made on data.
Identity is our single largest producer of that data.
Financial Crime is our single largest consumer of it.
Both sit in the same engineering organization, and today no one owns the path between them.
The result is a permanent tax on everything downstream: problems discovered late, by the people least able to fix them at source.
This role exists to remove that tax.
You will lead a team of 25+ Data Scientists, Machine Learning Engineers and Analytics Engineers across the Identity and Financial Crime domains
- What you will own
- Own the definition, generation, quality and consumption of data across the Identity and Financial Crime domains β what gets produced, what it means, what guarantees come with it, and who is accountable when those guarantees are not met.
- Make data quality an engineering discipline rather than a downstream cleanup exercise: producer/consumer contracts, freshness and integrity monitoring, lineage, a named owner for every critical dataset, and a real path from a detected anomaly to a fix at source.
You'll be measured on whether downstream teams stop discovering data problems by accident.
- Own the models and analytics behind how Zepz detects fraud and financial crime β feature engineering, model development, deployment, monitoring and retraining.
You own model performance in production, not just model development.
- Lead a mixed organization of Data Scientists, Machine Learning Engineers and Analytics Engineers.
These are three different crafts with three different career paths, and leading them well means understanding all three rather than favoring the one you came from.
- Partner as a peer with the Engineering Manager who owns the fraud detection platform and reports directly to the VP Engineering.
Your models run on their platform: you own model outcomes, they own platform outcomes, and you jointly own the seam between them.
- Partner with our central data function, which owns core data infrastructure and platform tooling.
You are a demanding customer and a collaborator, not a replacement β you build domain data products and quality practice on top of what they provide, and feed real requirements back to them.
- Set standards with Identity engineering teams who do not report to you.
Much of your impact depends on changing how data is produced upstream, which is influence work: making the cost of poor data visible with evidence rather than assertion, and making it easy for those teams to do the right thing.
- Create high-performing teams with a bias toward shipping without sacrificing reliability or trust in the numbers.
Use technical and data debt as a tool to move faster, while ensuring your teams have the space to build it right once they know what the right thing to build is.
- Hire, retain, and level up superlative talent to ensure our team remains world-class as we continue to scale and launch into new markets.
- Roll up your sleeves when models degrade or data breaks.
You'll need to discuss technical solutions, review work in depth, and help execute strategy with your leads.
In your first few months, you will
- Establish an honest, evidenced baseline of data quality across the identity-to-financial-crime path β what breaks, how often, who finds out, and what it costs in downstream hours and model degradation.
Bring the number, not the anecdote.
- Agree a written ownership model with the central data team and the Identity engineering leads: who owns which datasets, what the contracts are, and what happens when one is broken.
- Instrument the critical path, so data issues are detected by monitoring rather than by an analyst noticing that something looks odd.
- Get to know the models β how they perform, how they degrade, how retraining actually works today, and where risk is concentrated.
- Assess the team: identify the gaps, the flight risks and the people ready for more, and start hiring against a plan rather than against a vacancy.
- Promote a culture of ownership, accountability, and shipping.
What you bring to the table
- You have a track record of leading a data organization of comparable size (roughly 15β25 people) and of supporting the professional development of managers and senior-level ICs.
- Track record of leading mixed technical teams β Data Scientists, ML Engineers, Analytics Engineers β with genuine credibility across all three crafts.
- Demonstrable ownership of data quality at scale: contracts, observability, lineage, semantic consistency β with evidence of the downstream impact, not just the tooling you installed.
- Fluency with modern lakehouse and analytics engineering practice: Databricks (or equivalent), dbt-style transformation, feature store concepts and real-time feature serving, and the discipline of treating datasets as products with owners and SLAs.
- Experience with production ML in a real-time context: model monitoring, drift detection, and retraining pipelines.
- Proven experience with AWS at scale.
- Direct experience leading Fin Crime/Fraud or Identity engineering or data science teams (not just adjacent risk/security).
- The ability to get outcomes from teams that don't report to you.
We'd rather hear about a time you changed a producer team's behavior than a time you escalated.
- At least 8+ years of technical experience in a hybrid IC/management role.
- You can attract and retain top talent and lead teams with diverse skill sets.
- You work well with Engineering and Product leaders and are comfortable building effective relationships with folks outside of the Prod-Eng spheres.
- You have exceptional judgment; we're scaling quickly β you'll be required to make good tradeoffs.
- Fin Crime & Identity Domain
- Bring prior experience leading Fin Crime/Fraud or Identity teams β understanding of typologies (card fraud, ATO, first-party fraud, synthetic identity, money mule networks), the regulatory context, and how detection strategy trades off against customer experience
- Understand identity verification, KYC and onboarding data as a data domain, not just as a product surface
- Partner with Risk, Compliance, and Legal on regulatory obligations tied to fraud and identity controls
- Own incident response leadership when major fraud events, model failures, or data integrity failures occur
- Bonus points
- Have worked at a scaling startup previously.
- Have experience with data mesh or domain-oriented data ownership models in practice, not just in theory.
- Have experience working with stablecoin and cryptocurrencies.
- Have experience working with AI in a production environment.
- Have worked in environments with k8s / g RPC / Spring Boot / Java / Python.
- AI Literacy & Tools
As Zepz, we're building AI into how we work. We're on a journey to making AI utilisation streamline what we do. "For this position we are looking for:
- Proficient depth of knowledge around automation, AI and how those can be applied in your area of work β from accelerating analysis and insight generation, to improving how we design and deliver processes and communications.
- Practical experience using AI tools in a professional context β whether to connect information, draft content, build frameworks, or create insights from data.
We want people who actively use AI to increase productivity, not those who are waiting to be told to.
- Strong AI literacy β an understanding of what AI assistants can and cannot do, how to prompt effectively, and how to critically evaluate AI-generated output before using it.
- Awareness of the ethical considerations and responsible use of AI in the workplace, particularly in the context of sensitive data, customer experience, and compliance.
- Prior experience using Claude (Anthropic) is a nice-to-have but not essential β what matters most is that you are genuinely engaged with AI as a tool, comfortable experimenting, and eager to share what works with your wider team
If you want to join us in our journey to help break barriers in financial access and improve lives globally, there's no better place or time to join.
Our global team of 800+ people is spread across six continents.
We aspire to hire the best mix of people from former Olympians to You Tube influencers and we speak over twenty languages.
This incredible diversity isn't a bonus; it's the engine that lets us serve the world.
Ready to Apply? Letβs Go.
Director of Data Science & Engineering, Fraud Platform employer: Zepz
Zepz is an exceptional employer that fosters a dynamic and collaborative work culture, where innovation meets responsibility in the fast-paced FinTech sector. With a strong emphasis on employee growth, you will have opportunities to mentor peers and shape cutting-edge architecture while enjoying the flexibility of a hybrid work model in London. Join us to be part of a team that values AI literacy and delivers impactful solutions that enhance customer experiences.
StudySmarter Expert Adviceπ€«
We think this is how you could land Director of Data Science & Engineering, Fraud Platform
β¨Get Involved in Data Science Meetups
Tap into local data science meetups or workshops to connect with fellow enthusiasts and professionals. These events are goldmines for networking, and sometimes even lead directly to job openings at companies like Zepz!
β¨Show Off Your Projects
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β¨Leverage Professional Networks
Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like Zepz.
β¨Apply Directly through Our Website
When you find a suitable opening like Director of Data Science & Engineering, Fraud Platform at Zepz, make sure to apply directly through our website. It gives you an edge and shows you're keen to join our team. Plus, who doesnβt love a direct application? Itβs easier than navigating through job boards!
We think you need these skills to ace Director of Data Science & Engineering, Fraud Platform
Some tips for your application π«‘
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Donβt forget to include links to any GitHub repositories if applicable!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at Zepz, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why youβre a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Zepz. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at Zepz
β¨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
β¨Showcase Your Projects
Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, itβll really make us stand out!
β¨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at Zepz!
β¨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how weβd approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.