Data Scientist

Location: London

Department: Data

The role

A Machine Learning Data Scientist designs, builds, and deploys the scalable models and algorithmic systems powering Dojo’s automated decisions—from fraud prevention to operational efficiency. Where off-the-shelf scripts promise easy answers, your job is to build systems that survive production: balancing statistical rigor with clean software engineering to ensure our algorithms are robust, interpretable, and scale flawlessly.

On our Builder track, youll focus on deep algorithmic execution, numerical optimization, and software craft. Youre the person the team turns to when a prediction or optimization problem is hard, standard machine learning isnt enough, or a model degrades in the wild. You protect the integrity, reliability, and long-term health of our live production systems.

What you will do...

• Own workstreams: Drive technical projects from ideation to production, anticipating domain problems and aligning your roadmap with key commercial levers.
• Design pragmatic systems: Tackle complex modeling problems and design end-to-end ML systems, prioritizing robustness and maintainability over over-engineering.
• Formulate optimization solutions: Build numerical optimizers (LP/MIP), tune solvers, and design custom heuristics when exact objective functions or data are scarce.
• Maintain production rigor: Take responsibility for live models, monitoring for drift, preventing train/serve skew, and debugging subtle mathematical failures.
• Write production-grade code: Write well-structured, tested Python and SQL at scale, using Databricks and advanced data manipulation techniques.
• Drive AI workflows: Safely leverage AI tools to boost development productivity, while rigorously verifying output logic and optimization reasoning.

What you will bring...

• Mathematical grounding: Strong statistical background to reason about bias/variance and model evaluation, diagnosing subtle failures from first principles.
• Optimization & heuristics: Experience formulating business constraints into numerical optimization problems and designing custom heuristic algorithms.
• Python & SQL craft: Mastery of Python, Databricks, and the modern ML ecosystem, combined with writing highly efficient, scalable SQL.
• Production MLOps: Practical experience maintaining live models, including CI/CD pipelines, reusable feature architectures, and drift detection.
• Technical honesty: Commitment to evaluating models rigorously using metrics reflecting true business value, and communicating limitations transparently.
• Collaborative leadership: Track record of owning components end-to-end, cutting through ambiguity, mentoring junior peers, and setting engineering standards.