Descripción de la oferta
About the ProjectHands‑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 DoProfile 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.
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.
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.
SkillsExpert 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.
KnowledgeGDPR 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 Characteristics7+ 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.#J-18808-Ljbffr
