Principal Data Scientist

Are you passionate about applying data science, machine learning, and Generative AI to solve complex insurance challenges?

Do you enjoy building innovative data products and collaborating with cross-functional teams to deliver impactful, data-driven solutions?

About the Business

LexisNexis Risk Solutions is the essential partner in the assessment of risk. Within our Insurance vertical, we provide customers with solutions and decision tools that combine public and industry specific content with advanced technology and analytics to assist them in evaluating and predicting risk and enhancing operational efficiency. Our insurance risk solutions help drive better data-driven decisions across the insurance policy lifecycle, all while reducing risk. You can learn more about LexisNexis Risk at https://risk.lexisnexis.com/insurance

About our Team

You will join a wide global team of technical experts. The global Data Science function within Insurance has over 150+ Data Scientists, and 25 locally based in Dublin. This team has deep Data Science and insurance industry knowledge, supporting initiatives across Ireland, the UK and EU business, and helping to expand into other markets locally and internationally. The team plays a pivotal role in maintaining current product offerings, developing new solutions through R&D, and enabling internal and external decision-making through novel and advanced data science techniques. Our products help customers make informed decisions in pricing, underwriting, claims handling and fraud detection.

About the role

As a Principal Data Scientist, you will be an individual contributor but also serve as a senior technical leader supporting multiple teams, flexing across high-impact projects and driving innovation in data science, infrastructure, and product development. You will play a strategic role in shaping our advanced product roadmap, building scalable ML workflows, and collaborating across disciplines to deliver data-driven solutions that support our business goals. You will demonstrate high engagement both with other Data Scientists and less technical stakeholders, proactively seeking alignment on priorities. You will also demonstrate the ability to be proactive and push project information and updates, as well as sharing updates on market innovation at a level that is easy to ingest.

You will bring a blend of data science expertise, predictive modelling, and cloud infrastructure knowledge, and preferably have a decent grasp of insurance workflows, especially in pricing, underwriting, claims and fraud. Exceptional communication and collaboration skills are essential.

Responsibilities:

• Taking a proactive approach to explore any Generative AI or machine learning 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 structured, some unstructured) putting additional workflows on top of that data, which will feed additional products and gather additional insights.
• Support the design, testing, and documentation of best practices for data science 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) and adding embedding layers for enhanced predictive capabilities.
• Build and deploy new data products, including ETL pipelines and statistical models, in collaboration with our 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 requested.
• 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 Mathematics, Statistics, Physics, Computer Science or a related quantitative field; advanced degree preferred.
• 8+ years of experience in data science or a related field (MSc/PhD time can be weighted towards experience).
• Strong programming skills in Python or R (python preferred), with some experience in Azure ML Flow and/or Apache Spark and distributed computing.
• 3+ years of industry experience with mathematical modelling, building predictive models and implementing them into a production environment.
• 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 according to software engineering best practices.
• Strong SQL skills and familiarity with relational and non-relational databases.
• Proven ability to interpret, report and present 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.
• Experience with version control (Git), documentation standards, and collaborative development practices.
• MLOps; Azure ML/storage; Azure architecture; Databricks; Linux; BI tools; insurance exposure; model governance/explainability/ethical AI.

Learn more about the LexisNexis Risk team and how we work here

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