The mission
Build a state-of-the-art ML platform and the discipline around it.
- Models with a price tag. Every model has a business case and a measurable monetary outcome.
- Credit and decisioning models built with rigorous validation, champion/challenger testing and explainability.
- A production ML platform. Serving, monitoring, reproducibility and retraining are engineered, not improvised.
- Responsible AI, built in. Model risk, bias and explainability checks, with an independent sign-off gate before anything reaches production.
- Agent-first systems. Agents are production components with orchestration, guardrails, evals and observability.
- ◆ Models on governed data. You build on a point-in-time-correct feature store, not around it.
This is a business function. Every model carries monetary value, and you will run the function that way: compute budget, headcount and return on investment.
What you'll own
- The ML & MLOps team, from your first hire onward.
- Model-development standards and the validation methodology that stands up to model-risk and regulatory scrutiny.
- The ML platform behind decisioning services.
- A clear ownership line between feature production (data engineering) and model consumption, set together with the Head of Data Engineering and the Director.
You are
- A leader who loves data and loves building systems around it.
- Hands-on when needed, especially with AI on board. You understand the model, the pipeline and the serving layer.
- Experienced across the full ML lifecycle: development, validation, deployment, monitoring and retraining.
- Experienced in credit-scoring or underwriting modelling, or comparable high-stakes ML.
- Skilled in model-risk management and responsible-AI governance.
- Experienced in building and leading a team from zero.
- Fluent in English (B2+). [add years of experience: suggest 7+ years in ML, 3+ leading]
Bonus
- CCD2 and consumer-credit regulation · DORA/ICT risk · IFRS 9 implications for model outputs · fraud-detection ML · Databricks/Spark.
Why this one
- Seat at the table on a core leadership team.
- Build it right the first time. No legacy ML estate.
- Models that matter. Your work decides real money, not a dashboard.
- Real pace. A lean, AI-native organisation.