About the project_(description, duration, stage)_:
Hands-on Data Science Lead on a new engagement with a regulated UK & Ireland credit and lending company. The client has consolidated data from multiple business entities into a newly centralized, anonymized data lake and wants to turn it into validated risk analytics — delinquency, probability of default, credit-policy insight — plus an executive-facing natural-language insight layer.

This is a foundational data-science build, not an agentic-AI project. The early work is unglamorous and hands-on: validating data nobody can yet vouch for, then building defensible models on top. You are the senior data scientist the client is missing — you do the work and own the methodology, while leading a small pod and acting as the human-in-the-loop the client explicitly asked for.

Stage: pre-contract / scoping (Phase 1 = current-state assessment + data validation). Duration: multi-phase, multi-quarter ambition with strong extension probability.

Full-time engagement is preferable.

What you'll actually do_(example tasks)_:




- Profile the anonymized lake hands-on — interrogate tens-of-millions-of-row tables and reproduce and validate the team's existing descriptive statistics, so every number is traceable to source (the client cannot currently answer _“how do you know that's correct?”_).
- Build and validate the core risk models yourself:PD, delinquency / roll-rate, early-warning, segmentation and scorecards (WOE / IV, logistic regression, gradient boosting).
- Stand up the model-validation discipline that makes outputs audit-defensible: train / test / out-of-time splits, Gini / AUC / KS, calibration, stability (PSI), backtesting and full model documentation.
- Define feature logic with the Data Engineer and write it yourself in SQL / dbt / Python; specify the harmonized definitions the semantic layer must serve.
- Prototype and validate the natural-language insight layer (text-to-SQL / RAG over the semantic layer); check answer

📌 Data Science Lead (Madrid)
🏢 Neurons Lab
📍 Madrid