> Markdown version of [/jobs/ext/4573910-data-scientist](https://www.wearedevelopers.com/jobs/ext/4573910-data-scientist). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Scientist - **Company:** M&G - **Location:** Edinburgh, UK - **Contract:** Permanent contract - **Skills:** Big Data, Computer Programming, Databases, Data Cleansing, Data Governance, Data Systems, Apache Hive, Python (Programming Language), Machine Learning, Performance Tuning, Data Processing, Feature Engineering, Apache Spark, Pyspark, Data Analytics, Data Management, Data Pipelines, Databricks, Programming Languages - **Published:** November 5, 2026 - **Apply:** https://mgpru.wd3.myworkdayjobs.com/mandgprudential/job/Stirling/Data-Engineer--Databricks-_R19908 ## About the Role * Strong expertise in data science techniques, including machine learning and their application in financial services. * Proficiency in data tools, programming languages, and database technologies, in particular in Azure Databricks, Spark, Spark SQL and Python/pyspark. * Understanding of data governance, security, and data quality management principles. * Experience working with large, complex datasets and performing feature engineering and data preparation. * Proven ability to translate business problems into data science solutions and deliver measurable outcomes. * Strong problem-solving capability with a focus on accuracy and performance. * Ability to communicate technical concepts clearly to both technical and non-technical audiences. * Collaborative approach with the ability to influence decisions and guide others. * Experience working in Financial Services or a similar heavily audited environment with an understanding of operating controls and producing audit evidence Essential Skills: * Data pipeline development and optimisation * Data modelling and architecture (in Azure Databricks) * Programming and scripting for data processing (Spark, Spark SQL, Python, pyspark) * Data science and machine learning * Data governance and quality assurance * Problem-solving and analytical thinking * Stakeholder engagement and technical communication, including at design forums where relevant * Continuous improvement and innovation * Performance tuning and optimisation * People management ## Description This role is responsible for designing, building, and optimising data solutions that enable reliable and efficient access to data for use in actuarial modelling and assumption setting. Operating at an Experienced Colleague level, the role provides specialist expertise in data science, ensuring scalable, high-quality data pipelines and processes are in place to support the use of policyholder data for actuarial modelling and assumption setting. They will use their technical depth, financial domain awareness and delivery experience to lead the design and build of data workflows, drive process reengineering and transformation as well as embed the surrounding controls required for audit and risk assurance. They will apply machine learning and advanced analytics where they add measurable value. They will work to high standards for documentation and development in the team enabling proactive identification of inefficiencies in existing processes. The role contributes to shaping standards and best practices, ensuring data is accessible, governed, and aligned with business priorities. They will, alongside their own delivery, guide and develop junior team members - reviewing their analysis and code, providing technical advice, coaching them on controls and documentation. The role contributes to the continuous improvement of data processes, ensuring accuracy, consistency, and clarity in outputs while working within defined guidelines. Communication of findings is clear, structured, * Design, build, and maintain scalable data pipelines to validate and transform data to meet actuarial modelling needs. * Ensure data quality, integrity, and consistency through robust validation, monitoring, and governance practices. * Collaborate with stakeholders to understand data requirements and translate them into efficient and effective technical solutions. * Optimise data systems and processes to improve performance, reliability, and scalability. * Provide technical expertise and guidance on data science approaches, tools, and best practices. * Support integration of multiple data sources to create cohesive, accessible, and trusted datasets. * Contribute to the continuous improvement of data platforms, frameworks, and engineering standards.