Data Science Lead

Description

**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