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# Finance Data Scientist

- **Company:** Edenbrook
- **Location:** London, UK
- **Contract:** Permanent contract
- **Skills:** Microsoft Excel, Data Analysis, R (Programming Language), Power BI, SQL Databases, Build Management, Data Analytics, Data Pipelines, Databricks
- **Published:** September 3, 2026
- **Apply:** https://find.jobs/jobs-near-me/apply/ats-redirect/?id=2953327385-2

## About the Role

on manual and Excel-based processes by improving data automation and efficiency.* Collaborate with finance and actuarial teams to identify opportunities for data-driven improvements.* Develop Power BI dashboards and reports to communicate key insights clearly to stakeholders.* Monitor industry trends and emerging technologies relevant to data, analytics and insurance.* Work with IT to ensure financial data is securely stored, managed and analysed.* Maintain data documentation and comply with governance, quality, control and audit standards.The ideal candidate should have but not limited to;* Background working within the Insurance industry.* Technical background in Finance, Actuarial Science, Data Analytics or a related field* Practical experience with Power BI, SQL, R and Databricks* Solid understanding of financial and/or actuarial processes and dataDiscipline(s): Change ManagementJob type: Permanent

## Description

We are currently partnering with a rapidly growing speciality Insurer to recruit a Finance Data Scientist. The successful candidate will support and enable the transformation of the finance and actuarial function by applying data science techniques to financial data, automating manual processes, and building predictive and analytical models and dashboards that clearly communicate insights to stakeholders.Key responsibilities;* Collect, organise and analyse large financial and actuarial datasets from multiple sources.* Generate data-driven insights to support business decisions and improve financial outcomes.* Build predictive models to identify financial risk factors and improve forecasting accuracy.* Develop analytical models to identify data quality issues, trends and exceptions.* Review financial and actuarial processes to identify automation and process improvement opportunities.* Design and build automated data workflows using Databricks and scalable data pipelines.* Reduce reliance


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