Senior Data Scientist

Location: hybrid 3 days per week onsite in either London or Sheffield (can be negotiated to 2 days per week for exceptional candidates in exceptional circumstances) Remuneration: either £450/day INSIDE IR35, or £90-£100k permanent employed

Our client is a fast-growing international consultancy specialising in data, AI and advanced analytics for major financial institutions. With programmes running across the UK and Europe, they are expanding their team to support large-scale data science and modelling initiatives.

This is a Senior Data Scientist/Analyst role within their global banking client's developer experience team, supporting a new organisation-wide analysis initiative. This role sits at the intersection of data engineering and analytics, and requires strong, critical data processing and presentation skills to translate raw, ambiguous data into insights that directly drive strategic decisions across the organisation.

Drive organisation-wide analysis, autonomously conducting gap analyses and translating ambiguous business needs into clear technical roadmaps. Manage expectations directly with users and data owners, keeping delivery aligned to business priorities.

Architect, build, and scale robust data pipelines using Python and SQL across platforms such as BigQuery and PostgreSQL. Work within established team standards for data extraction, complex aggregation, and resilient exception management, contributing to their ongoing improvement.

Apply advanced, critical quantitative methods (Pandas, NumPy) to process raw data and derive actionable insights. Implement automated frameworks to ensure enterprise-grade data quality and continuous validation.

Go beyond basic reporting to translate raw statistical findings into insights that clearly drive strategic business decisions.

Developer Experience & DORA metrics: A working knowledge of DORA metrics (Lead Time to Deploy, Release Frequency) will be nice. Knowledge of Development Practices/Pipeline/Methodologies will be needed.

• Knowledge of workflow toolkits such as Apache Airflow, Dagster, or similar.
• Active use of modern LLMs (Claude, GitHub Copilot) to accelerate development and pioneer AI-assisted coding practices within the team.
• Version control: Enforce version control best practices (Git/GitHub), lead code reviews, and integrate pipelines into CI/CD workflows.
• Cloud architecture: Prior experience deploying, optimising, working with cloud environments.