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. Reporting: Engagement lead / CTO (@Alex Honchar); leads the pod's Data Engineer(s) and the client's offshore data team. 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 correctness and add guardrails. Run a credit-policy / cut-off analysis showing where the client could tighten policy or reduce delinquency — the concrete insight their own clients keep asking for. Lead a small pod (Data Engineer, client's junior offshore data people): set tasks, review work, be the quality bar and the human-in-the-loop. Front the client's data leadership: present findings, explain methodology to non-technical executives, and shape the phased roadmap / SoW. Skills (hands-on first) Expert Python for data science (pandas / Polars, scikit-learn, statsmodels) and strong SQL over large tables Credit-risk / financial modeling: scorecards, PD, delinquency, segmentation, model validation and governance Data validation, profiling and feature engineering on messy enterprise data dbt / semantic modeling; partnering with data engineering on the harmonization layer GenAI insight layer: text-to-SQL, RAG over structured data, evaluation and guardrails Methodology, lineage and documentation that survives audit; able to explain it to executives Leadership of small delivery pods and distributed / offshore teams Knowledge GDPR fundamentals (anonymization vs pseudonymization, UK / EU data residency) AWS analytics stack and Well-Architected (Analytics, Security) for BFSI UK / EU credit & lending regulatory context (FCA, model governance, fair-lending / explainability) — strong plus Familiarity with credit-bureau / scoring data products — strong plus Experience Key characteristics (ideally 4/4): Hands-on data science at enterprise scale Worked with financial-services / credit clients or in-house at a credit / lending company Cloud hyperscaler experience (AWS preferred) Technology consulting / client-facing delivery background Role-specific characteristics: 7+ years hands-on data science, with real credit-risk / financial modeling Experience building and validating models in a regulated, audited context Led small data-science teams while still coding personally Demonstrably comfortable doing the data-cleaning grunt work themselves, not just directing it