Senior Data Scientist

We’re partnering with a rapidly growing technology-led financial services company using data, machine learning and AI to improve complex financial decision-making.

They’re looking for a Senior Data Scientist to build and deploy production machine learning and AI systems across a range of high-impact financial workflows. You’ll own projects from research and experimentation through to production, working across predictive modelling, forecasting, optimisation and emerging AI technologies.

This is a highly hands-on role where your work will directly influence important business decisions. You’ll work closely with engineering and senior business stakeholders, turning complex datasets and quantitative models into reliable systems and clear, actionable insights.

What You’ll Do:

• Build and deploy predictive models for risk assessment, forecasting, classification and optimisation.
• Develop AI-powered workflows and intelligent systems with appropriate guardrails and human oversight.
• Build and orchestrate LLM-powered workflows combining structured and unstructured data.
• Own the full model lifecycle from experimentation and validation through to deployment, monitoring and iteration.
• Establish robust evaluation, versioning and monitoring practices to maintain model reliability.
• Develop model-serving APIs and contribute to scalable production systems.
• Work with large and complex datasets to identify patterns, signals and opportunities for improved decision-making.
• Build dashboards and reporting tools that translate quantitative outputs into actionable insights.
• Partner closely with Engineering and senior business stakeholders to turn complex problems into effective data science solutions.
• Present modelling and analytical findings clearly to technical and non-technical audiences.

Who We’re Looking For:

• 6+ years' experience in Data Science, Quantitative Modelling or AI/ML, with meaningful production experience.
• Strong experience with forecasting, regression, classification, optimisation and/or risk modelling.
• Hands-on experience with models such as XGBoost or LightGBM, alongside model interpretation techniques such as SHAP.
• Advanced Python skills, particularly Pandas and Scikit-learn.
• Strong SQL and experience working with relational or analytical databases.
• Proven experience taking ML models or AI systems from experimentation into production.
• Understanding of MLOps, including experiment tracking, model versioning, deployment and monitoring.
• Strong software engineering fundamentals and the ability to debug complex data and model pipelines.
• Familiarity with LLMs, AI agents and intelligent workflows.
• Strong communication skills and the ability to translate complex quantitative work into clear business insights.