About the Business: LexisNexis Risk Solutions is the essential partner for risk assessment. Within our Insurance business, we provide customers with solutions and decision tools that combine public, proprietary and industry specific content with advanced technology and analytics to assist them in evaluating and predicting risk and enhancing operational efficiency. Our solutions help drive better data-driven decisions across the insurance lifecycle, all while reducing risk and optimising processes. You can learn more about LexisNexis Risk at the link
https://risk.lexisnexis.com/insurance
About the Team: You will join a team of technical experts with deep Data Science and insurance industry knowledge, supporting initiatives across the UKI and EU business, and expanding into other adjacent and international markets. The team plays a pivotal role in maintaining current product offerings, developing new solutions through R&D, and enabling internal decision-making through advanced data science techniques.
Our products help customers make informed decisions in pricing, underwriting, and fraud detection.
About the role: As a Principal Data Scientist, you will serve as a senior technical leader, flexing across high-impact projects and driving innovation in analytics, infrastructure, and product development. You will play a strategic role in shaping our cloud migration, building scalable ML workflows, and collaborating across disciplines to deliver data-driven solutions that support our business goals.
You will bring a blend of data science expertise, predictive modelling, and cloud infrastructure knowledge, and hopefully have a decent grasp of insurance practices, especially in pricing, underwriting and fraud. Exceptional communication and collaboration skills are essential.

Responsibilities:

• Taking a proactive approach, to explore any AI product solutions (or efficiencies) developed by our US colleagues and exploring their application for the local business.
• Proactively exploring other AI solutions to identify and explore their viability in enhancing what we do today.
• Act as a data science expert, contributing to and guiding multiple projects across domains.
• Support our cloud migration, collaborating with technology teams to ensure analytics infrastructure aligns with long-term goals.
• Taking large quantities of data (mostly structure, some unstructured) putting additional workflows on top of that data, which will feed additional products and gather additional insights.
• Working with, enhancing and collaborating various types of data for further enrichment, including Connected Car / Telematics data
• Support the design, testing, and document best practices for analytics in the cloud to ensure a smooth transition and operational excellence.
• Develop and prototype innovative solutions through our infrastructure to improve accuracy, efficiency, and productivity.
• Supporting large scale benchmarking opportunities and sharing valuable insights on the unique data we hold.
• Building Predictive models for the insurance industry to test our products (claim frequency and severity)
• Build and deploy new data products, including ETL pipelines and statistical models, in collaboration with technology teams.
• Work with our technology partners to help improve and implement end-to-end ML workflows, from data ingestion to model deployment and monitoring.
• Collaborate with stakeholders across product, technology, and data engineering to align priorities and vision.
• Communicate complex analytical results clearly and effectively to both technical and non-technical audiences.
• Develop solutions using both open-source tools and proprietary platforms.
• Present project updates internally and externally, as needed.
• Help define project requirements, timelines, and execution plans in collaboration with cross-functional teams.
• Mentor team members and contribute to a culture of technical excellence and continuous learning.

Requirements:

• Degree in Computer Science, Mathematics, Statistics, or a related quantitative field; advanced degree preferred.
• Significant experience in data science or a related field (MSc/PhD time can be included).
• Exceptional programming skills in Python or R, with some experience in Azure ML Flow and/or Apache Spark and distributed computing.
• Expertise in data processing, including extraction, cleaning, transformation, and handling diverse formats (e.g., Avro, Parquet, JSON, XML).
• Experience building robust, testable data pipelines and writing clean, maintainable code.
• Excellent SQL skills and familiarity with relational and non-relational databases.
• Proven ability to interpret data with a focus on consistency, integrity, and business value.
• Excellent communication skills, with the ability to present findings to varied audiences.
• Experience mentoring others and leading technical workstreams.
• Familiarity with version control (Git), documentation standards, and collaborative development practices.

Preferred Experience

• Experience working with telematics (telemetry data) is an advantage, but not required.
• ML workflows and MLOps practices
• Azure ML and Azure data storage services (Blob Storage, Data Lake Gen2).
• Understanding of Azure architecture and ability to collaborate with architects and engineers.
• Experience with Databricks for large-scale data processing.
• Familiarity with Linux systems and command-line tools.
• Experience with BI tools such as Power BI, R Shiny, or similar.
• Exposure to insurance data and basic understanding of insurance modelling.
• Knowledge of model governance, explainability, and ethical AI practices is a plus.

Learn more about the LexisNexis Risk team and how we work
here
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• Taking a proactive approach, to explore any AI product solutions (or efficiencies) developed by our US colleagues and exploring their application for the local business.
• Proactively exploring other AI solutions to identify and explore their viability in enhancing what we do today.
• Act as a data science expert, contributing to and guiding multiple projects across domains.
• Support our cloud migration, collaborating with technology teams to ensure analytics infrastructure aligns with long-term goals.
• Taking large quantities of data (mostly structure, some unstructured) putting additional workflows on top of that data, which will feed additional products and gather additional insights.
• Working with, enhancing and collaborating various types of data for further enrichment, including Connected Car / Telematics data
• Support the design, testing, and document best practices for analytics in the cloud to ensure a smooth transition and operational excellence.
• Develop and prototype innovative solutions through our infrastructure to improve accuracy, efficiency, and productivity.
• Supporting large scale benchmarking opportunities and sharing valuable insights on the unique data we hold.
• Building Predictive models for the insurance industry to test our products (claim frequency and severity)
• Build and deploy new data products, including ETL pipelines and statistical models, in collaboration with technology teams.
• Work with our technology partners to help improve and implement end-to-end ML workflows, from data ingestion to model deployment and monitoring.
• Collaborate with stakeholders across product, technology, and data engineering to align priorities and vision.
• Communicate complex analytical results clearly and effectively to both technical and non-technical audiences.
• Develop solutions using both open-source tools and proprietary platforms.
• Present project updates internally and externally, as needed.
• Help define project requirements, timelines, and execution plans in collaboration with cross-functional teams.
• Mentor team members and contribute to a culture of technical excellence and continuous learning