Company Description
Build ML that makes money and stands up to a regulator.
We are building a scale-up inside InPost, and AI will flow through it. You would join the core Data & AI leadership team and build the ML and MLOps function from zero, with the InPost executive team backing it. Our word is extreme ownership: you own a model from first idea to its profit and loss impact.
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.
Summary
Build the ML and MLOps function from zero, including production ML platform for credit and decisioning models with serving, monitoring, and retraining. Requires 7+ years in ML with 3+ years leading, plus high-stakes modeling and model-risk governance.
Responsibilities
Build ML and MLOps function from zero; own ML platform, serving, monitoring, reproducibility, retraining; own model-development standards and validation methodology; lead ML & MLOps team; run function by compute budget, headcount and ROI
Qualifications
Hands-on with model, pipeline and serving layer; full ML lifecycle experience; credit-scoring or underwriting modelling; model-risk management and responsible-AI governance; team building from zero; Fluent in English (B2+)
Experience requirements
7+ years in ML; 3+ years leading; full ML lifecycle: development, validation, deployment, monitoring and retraining; credit-scoring or underwriting modelling or comparable high-stakes ML; model-risk management and responsible-AI governance; building and leading a team from zero