An established and rapidly growing UK investment and financial services platform, providing digital investment, pension and dealing solutions to both individual customers and professional advisers, is looking for an experienced Data Scientist who can take complex or loosely defined business questions and turn them into useful analysis and clear recommendations.

This is not purely a modelling role. You will work directly with senior stakeholders to understand business challenges, explore large and complex datasets and determine the most appropriate analytical approach.

Using Python, SQL, statistics and machine-learning techniques, you will identify patterns, test hypotheses, build predictive or forecasting models and translate the findings into practical business actions.

The role suits someone who enjoys working with ambiguity and can connect technical analysis with the bigger commercial picture.

Key Responsibilities
Lead analytical projects from initial business question through to recommendation.
Analyse complex customer, operational, financial and risk-related datasets.
Develop statistical and predictive models using Python and SQL.
Apply techniques such as forecasting, regression, classification, segmentation and propensity modelling.
Work with stakeholders to define requirements and determine the right analytical approach.
Assess model performance, uncertainty, bias and limitations.
Present complex findings clearly to senior and non-technical audiences.
Produce concise analytical papers, recommendations and decision-support materials.
Deliver useful early insight quickly and refine analysis as new information becomes available.
What We’re Looking For
You will have substantial experience in Data Science, Data Analytics, Insight Analytics or a comparable analytical role, together with:

Advanced Python and strong SQL skills.
Strong statistical and analytical knowledge.
Practical experience applying data-science techniques to real business problems.
Experience working with large or complex datasets.
Strong stakeholder management and requirements-gathering skills.
The ability to translate technical findings into commercial implications and practical recommendations.
Excellent written communication and confidence presenting analysis to senior stakeholders.
A pragmatic approach to prioritisation, experimentation and problem-solving.
Experience with time-series forecasting, churn, customer lifetime value, propensity, segmentation or anomaly-detection models would be valuable.

Knowledge of Snowflake or similar cloud data platforms, Streamlit and Power BI would also be advantageous, as would previous experience within financial services or another regulated, data-intensive sector.

Working Model
The position is based in Manchester and operates on a hybrid basis. New employees should expect greater office attendance during their initial onboarding period.

The package includes annual leave with additional service-related entitlement, pension contributions, discretionary bonus, employee share schemes, healthcare benefits, enhanced family leave and ongoing professional development.