Principal Data Scientist

Overview

Are you passionate about leveraging data science, machine learning, and Generative AI to tackle challenging insurance problems? We are looking for a Principal Data Scientist to develop innovative data-driven products and collaborate across teams to deliver impactful solutions in the insurance sector.

About Us

LexisNexis Risk Solutions is a trusted partner in risk assessment, offering solutions that integrate public and industry-specific data with advanced analytics and technology to help clients across the insurance lifecycle make better decisions and reduce risk. Our Insurance vertical supports pricing, underwriting, claims, and fraud detection with data-driven insights.

Team and Role

Join a global team of over 150 Data Scientists with 25 based in Dublin, supporting markets in Ireland, the UK, EU, and beyond. This team plays a strategic role in maintaining and evolving current products and developing new ones using cutting-edge data science methods.

As a Principal Data Scientist, you will serve as a senior technical leader, working across multiple projects to drive innovation in data science, infrastructure, and product development. You will be responsible for influencing the product roadmap, building scalable machine learning workflows, and collaborating with cross-disciplinary teams to advance our business goals.

Key Responsibilities

• Proactively investigate Generative AI and machine learning solutions pioneered by US teams, assessing their potential for local business applications.
• Identify and evaluate other AI technologies to enhance current processes and offerings.
• Provide expertise and guidance on multiple data science projects spanning various domains.
• Collaborate with technology teams to support cloud migration and ensure alignment of analytics infrastructure with strategic objectives.
• Manage and augment large structured and unstructured datasets with additional workflows to support new product development and insight generation.
• Develop best practices for data science in the cloud, ensuring seamless transition and operational excellence.
• Design, prototype, and implement innovative solutions to improve model accuracy, efficiency, and productivity leveraging existing infrastructure.
• Conduct large-scale benchmarking and share insights derived from unique data assets.
• Create predictive models tailored for insurance applications such as claim frequency and severity, incorporating embedding layers for improved performance.
• Work closely with technology partners to build and deploy end-to-end ML pipelines including ETL, modeling, deployment, and monitoring.
• Align priorities and project vision through collaboration with stakeholders in product, technology, and data engineering.
• Communicate analytical findings clearly to technical and non-technical audiences.
• Utilize both open-source and proprietary tools for model and product development.
• Present project progress and findings internally and externally as required.
• Define project requirements and timelines collaboratively with cross-functional teams.
• Mentor colleagues and foster a culture of technical excellence and continuous learning.

Required Qualifications

• A degree in Mathematics, Statistics, Physics, Computer Science, or a related quantitative discipline; advanced degrees preferred.
• At least 8 years of experience in data science or related analytical roles; relevant MSc or PhD experience may be counted.
• Strong programming capabilities in Python (preferred) or R, with practical experience in Azure ML Flow, Apache Spark, and distributed computing environments.
• A minimum of 3 years implementing mathematical and predictive models in industry production settings.
• Advanced skills in data processing including extraction, cleansing, transformation, and working with diverse data formats such as Avro, Parquet, JSON, and XML.
• Experience developing robust, maintainable, and testable data pipelines adhering to software engineering best practices.
• Proficiency in SQL and working knowledge of both relational and NoSQL databases.
• Demonstrated ability to analyze, interpret, and present data with a focus on maintaining data integrity and deriving business value.
• Excellent verbal and written communication skills to engage varied audiences effectively.
• Track record of mentoring peers and leading technical initiatives.
• Familiarity with Git version control, documentation standards, and collaborative software development methodology.
• Experience with MLOps, Azure ML/storage, Azure architecture, Databricks, Linux, BI tools, insurance exposure modeling, and principles of model governance, explainability, and ethical AI.

Additional Information

Location: Dublin, Ireland (Rockfield Central)

Compensation Range: €75,300 - €125,600 annually

Benefits are provided in line with country-specific offerings to ensure well-being and career satisfaction.