About the role
Job Description
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.
Qualifications
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.
Additional Information
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.