Lead Data Scientist

Overview

As Lead Data Scientist at Cotality, you will set the technical direction for core data products used by major financial institutions and help shape a new data platform. You will lead a team of data scientists, drive innovative predictive modelling, and ensure production-grade, scalable models. You will collaborate with Product, Sales, and Leadership to translate business needs into strong analytical solutions, maintaining best practices and model quality. This role offers impact across finance-focused property data platforms and strategic projects.

Pay / Benefits

• flexible working arrangements
• generous leave
• private medical cover
• pension scheme
• annual wellbeing allowance
• parential leave

Responsibilities

• Design and deploy advanced predictive models using sophisticated statistical and machine learning techniques to solve high-impact business problems
• Provide technical leadership and mentorship to fellow data scientists, elevating team capabilities
• Own end-to-end model lifecycle from R&D to production deployment
• Collaborate with senior stakeholders across Product, Sales, and Leadership to translate business needs into analytical solutions
• Advise on best practices in model development, validation, and maintenance to ensure quality and integrity of outputs

Key requirements

• 8+ years of data science experience with deployed, high-impact ML models in a commercial setting
• Expert-level knowledge of statistical analysis, predictive modelling, and ensemble methods (e.g., Gradient Boosted Trees, Random Forests)
• Strong proficiency in Python (Pandas, Scikit-Learn, XGBoost/LightGBM) and SQL
• Experience handling very large, complex datasets; geospatial data experience is a plus
• Proven ability to lead technically, mentor others, and guide complex projects
• Excellent communication skills to explain technical concepts to non-technical stakeholders and influence decisions

• leadership and mentorship
• strong communication and collaboration
• ability to translate business needs into analytical solutions
• Python for data science (Pandas, Scikit-Learn, XGBoost/LightGBM)
• SQL
• Ensemble modelling techniques (Gradient Boosted Trees, Random Forests)

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