ML/DataOps Manager in London

At FDJ UNITED, we don't just follow the game, we reinvent it.

FDJ UNITED is one of Europe’s leading betting and gaming operators, with a vast portfolio of iconic brands and a reputation for technological excellence.

With more than 5,000 employees and a presence in around fifteen regulated markets, the Group offers a diversified, responsible range of games, both under exclusive rights and open to competition.

We set new standards, proving that entertainment and safety can go hand in hand.

Here, you’ll work alongside a team of passionate individuals dedicated to delivering the best and safest entertaining experiences for our customers every day.

We’re looking for bold people who are eager to succeed and ready to level-up the game.

If you thrive on innovation, embrace challenges, and want to make a real impact at all levels, FDJ UNITED is your playing field.

Join us in shaping the future of gaming. Are you ready to LEVEL-UP THE GAME?

About the role

FDJ United’s ambition is to be the most insight-driven gambling company, and in recent years we’ve invested heavily in our data and machine learning capabilities.

Focusing on our Sportsbook product, we’re looking for a Data Ops / MLOps Manager to lead the evolution of our ML platform and data operations capability, enabling faster, more reliable, and scalable delivery of data and machine learning products.

This role will be central to building and operating the foundations that support end-to-end machine learning workflows — from research and experimentation through to production deployment and monitoring.

You’ll lead a team responsible for Data Ops and MLOps practices, aligning closely with Data Engineering, Machine Learning Engineering, Quants, and Data Science teams to ensure seamless collaboration and delivery.

As we scale our personalisation, customer risk, and trading optimisation capabilities, you’ll play a key role in shaping the tooling, standards, and processes that enable teams to iterate quickly while maintaining high levels of reliability, governance, and compliance.

• What You’ll Do
• Lead and grow a team of Data Ops and MLOps engineers, providing technical direction, coaching, and career development.
• Define and drive the strategy for Data Ops and MLOps capabilities across the sportsbook data platform.
• Build and evolve the platform and tooling that supports end-to-end machine learning lifecycle management (development, deployment, monitoring, and retraining).
• Establish best practices for data and machine learning workflows, including automated testing, deployment, and rollback strategies.
• Work with Dev Ops to evolve infrastructure as code practices (e. g. Terraform, Cloud Formation) to ensure scalable and reproducible environments.
• Collaborate with Data Engineers, Quants, and ML Engineers to improve developer experience, platform usability, and delivery velocity.
• Implement robust monitoring, observability, and alerting across data pipelines and ML models (including model performance and drift).
• Drive standardisation of tooling and workflows across teams, balancing flexibility with consistency.
• Ensure data and model governance practices are embedded, including reproducibility, lineage, discoverability, and compliance.
• Partner with product and business stakeholders to align platform capabilities with strategic priorities.
• Manage platform reliability, performance, and cost efficiency within AWS.
• What You’ll Work On
• A modern data and machine learning platform supporting both batch and real-time use cases.
• Tooling and infrastructure for experiment tracking, model deployment, feature management, and monitoring.
• A Data Lakehouse architecture supporting both analytical and ML workloads.
• CI/CD pipelines and infrastructure that enable rapid, safe iteration for data and ML teams.
• Cross-team enablement to improve how data and ML products are developed, deployed, and operated.

About You

We think that to be successful in this role you will be able to demonstrate many of the following attributes:

• 5+ years of relevant commercial experience, including experience in Data Ops, MLOps, or platform engineering roles.
• Proven experience leading or mentoring engineers in a technical environment.
• Strong understanding of data engineering and machine learning workflows and how they operate in production.
• Experience implementing CI/CD pipelines for data and/or machine learning systems.
• Experience with Infrastructure as Code (Terraform, Cloud Formation, or similar).
• Strong experience with cloud platforms, ideally AWS.
• Experience with distributed data processing technologies (e. g. Spark) and streaming platforms (e. g. Kafka).
• Ability to operate at both a strategic and hands-on level when required.
• Strong communication and stakeholder management skills, with the ability to align cross-functional teams.
• Proactive mindset with the ability to navigate ambiguity and drive continuous improvement.
• Nice to Have
• Experience in sports betting, trading platforms, or regulated industries.
• Familiarity with modern ML tooling (e. g. feature stores, model registries, experiment tracking platforms).
• Knowledge of data architecture patterns such as Data Mesh or medallion architecture.
• Experience with observability and monitoring tools for data and ML systems.
• Experience building internal platforms or developer tooling.
• Passion for mentoring and building high-performing teams.
• Why This Role Matters

This role is critical to scaling how we build and operate data and machine learning products across sportsbook.

By establishing strong Data Ops and MLOps foundations, you’ll enable teams to deliver high-quality, reliable, and production-ready solutions faster.

This directly impacts our ability to personalise customer experiences, manage risk effectively, and make better data-driven decisions across the business.

We believe talent knows no boundaries.

Our hiring process focuses solely on your skills, experience, and potential to contribute to our team.

We welcome applicants from all backgrounds and evaluate each candidate based on merit, regardless of personal characteristics as the age, gender, origin, religion, sexual orientation, neurodiversity or disability.