data scientist

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