Data Science Lead

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‑million‑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