Lead Data Scientist

My client is seeking a Lead Data Scientist to own and drive the end-to-end data science strategy across high-impact business domains (e.g., risk, fraud, affordability, customer value).

This role will translate complex, real-world financial data into production-grade machine learning systems that deliver measurable commercial and customer outcomes.

The successful candidate will lead model development from ideation through deployment and monitoring, working closely with Data Engineers, Analysts, Product, Risk, and Technology stakeholders. They will set standards for experimentation, governance, and MLOps while mentoring and developing other data scientists within the function.

This is a hands‑on technical leadership role - both strategic and delivery-focused.

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• Define and evolve the data science roadmap aligned to business priorities.
  
• Identify high-value ML use cases and translate commercial problems into scalable analytical solutions.
  
• Lead end-to-end model lifecycle delivery: problem framing, feature engineering, experimentation, validation, deployment, monitoring, and iteration.
  
• Establish best practices for experimentation, evaluation, reproducibility, and documentation.
  
• Set standards for model governance, explainability, monitoring, and auditability within a regulated financial environment.
  

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• Architect and develop robust ML models (e.g., classification, regression, anomaly detection) using Python and cloud-based tooling.
  
• Design scalable feature pipelines in collaboration with Data Engineering.
  
• Lead productionisation efforts, including pipeline design, model versioning, and monitoring frameworks.
  
• Implement safe deployment strategies (e.g., champion/challenger models, shadow runs, A/B testing).
  
• Ensure model performance, drift detection, and continuous improvement processes are embedded.
  

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• Partner with Risk, Compliance, Product, and Technology teams to ensure solutions are commercially viable and regulator‑ready.
  
• Communicate complex modelling approaches and outcomes clearly to senior stakeholders and non‑technical audiences.
  
• Influence strategic decision‑making through insight and evidence‑based recommendations.
  

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• Own model documentation (model cards, lineage, assumptions, validation evidence).
  
• Embed privacy‑by‑design, fairness, and bias monitoring practices.
  
• Operate confidently within FCA‑regulated environments and support audit and regulatory requirements.
  

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• Mentor and coach junior and mid‑level data scientists.
  
• Lead code reviews and promote engineering best practice (Git, testing, CI/CD awareness).
  
• Contribute to hiring, technical assessment, and capability development.
  
• Foster a culture of curiosity, collaboration, and high performance.
  

The ideal candidate is commercially minded, technically strong, and delivery-focused. They understand that high‑quality models create value only when deployed safely and embedded into business processes.

They will be comfortable owning ambiguity, setting direction, and raising technical standards. They will combine statistical rigour with pragmatic decision‑making and be confident influencing senior stakeholders.

They will enjoy mentoring others and developing team capability alongside delivering impactful work.

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• Significant hands‑on experience building and deploying machine learning models into production environments.
  
• Strong Python expertise (e.g., pandas, scikit‑learn, ML frameworks) with production‑quality coding standards.
  
• Advanced SQL skills and deep understanding of relational data.
  
• Strong statistical foundations and model validation expertise.
  
• Experience working within cloud‑based data platforms (AWS or equivalent).
  
• Demonstrable experience productionising models and implementing monitoring frameworks.
  
• Experience operating within a regulated environment (financial services preferred).
  
• Ability to communicate effectively with senior stakeholders.
  
• Experience mentoring or leading other data scientists.
  

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• Experience in credit risk, fraud detection, affordability modelling, or payments analytics.
  
• Familiarity with model risk management frameworks.
  
• Exposure to MLOps tooling (CI/CD pipelines, automated testing, model registries).
  
• Experience with model explainability techniques (e.g., SHAP, LIME).
  
• Experience shaping data science roadmaps or leading multiple concurrent initiatives.
  

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• Hybrid working
  
• Training and development budget
  
• Flexible working