Data Science Lead

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Neurons Lab

WebsiteLinkedInAll Job Openings

51 - 200 employees

💰 Corporate Round on 2022-10

Neurons Lab is a globally distributed AI R&D company that helps deep tech innovators to accelerate data-driven products development and launch. Our team has expertise in fundamental sciences, full-stack AI/ML engineering, and product design. Such a rare combination and access to scarce talent allows Neurons Lab to build disruptive solutions for clients in HealthTech and EnergyTech industries. Neurons Lab operates within a proprietary delivery framework that is tailored to the innovation environment: fierce competition, tight timelines, little-to-none datasets, and the necessity to generate novel solutions.

📋 Description

• 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. • Build and validate the core risk models yourself: PD, delinquency / roll-rate, early-warning, segmentation and scorecards. • 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; 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.

🎯 Requirements

• 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 • Expert Python for data science (pandas / Polars, scikit-learn, statsmodels) • 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 • 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.

🏖️ Benefits

• Full-time engagement is preferable.