We are looking for a Principal Data Scientist I to join our Data Science / Embedded AI Innovation team at LexisNexis. This role is ideal for a highly skilled and experienced generalist data scientist who can independently lead complex AI, machine learning, NLP, analytics, experimentation, evaluation, cloud, and application development initiatives.

In this role, you will provide technical and execution leadership across applied AI solutions, generative AI use cases, machine learning models, data science experimentation, application development, AWS-based solution design, and production evaluation frameworks. You will play a key role in shaping technical direction, improving AI quality, defining measurable outcomes, and driving best practices for responsible, scalable, and reliable AI delivery.

You will also help manage the team’s technical work by coordinating priorities, breaking down work, identifying dependencies, tracking execution, and helping remove blockers. This is a senior individual contributor role and does not include direct people-management responsibility.

The ideal candidate is not limited to traditional data science work. They should be able to move from problem discovery to modeling, experimentation, application development, cloud deployment, evaluation, and production support. They should be comfortable building AI-enabled applications and services that can be used by internal teams, product teams, and customers.

What You’ll Do

Applied AI, Machine Learning & Data Science

• Lead design, development, experimentation, and evaluation of AI/ML solutions across multiple product and engineering initiatives.
• Operate as a generalist across machine learning, NLP, generative AI, analytics, experimentation, model evaluation, application development, cloud engineering, and applied data science use cases.
• Develop and improve models, prompts, retrieval strategies, evaluation methods, and data-driven approaches for product-facing AI capabilities.
• Apply statistical, machine learning, and experimentation techniques to solve complex customer and business problems.
• Translate ambiguous business problems into structured data science approaches, measurable objectives, and executable delivery plans.

AI Application Development & Product Prototyping

• Design, build, and iterate on AI-enabled applications, prototypes, internal tools, APIs, services, and proof-of-concepts.
• Develop working solutions that demonstrate business value and can evolve into production-ready capabilities.
• Build application components that integrate models, data pipelines, prompts, retrieval systems, evaluation workflows, and user-facing experiences.
• Partner with engineering teams to transition prototypes and data science solutions into scalable production systems.
• Contribute to backend services, APIs, automation scripts, evaluation dashboards, and workflow tools needed to support AI delivery.
• Ensure applications are designed for reliability, maintainability, observability, security, and scalability.

AWS & Cloud-Based Solution Development

• Design and develop cloud-native AI/ML and data science solutions using AWS services.
• Use AWS capabilities such as S3, Lambda, API Gateway, IAM, CloudWatch, Redshift, Bedrock, Glue, ECS, ECR, and related services where appropriate.
• Build scalable cloud-based workflows for AI experimentation, evaluation, data processing, model integration, and application deployment.
• Partner with platform and engineering teams to ensure AWS-based solutions follow security, compliance, cost, and operational standards.
• Improve performance, cost efficiency, reliability, and maintainability of cloud-based AI and data science applications.
• Support production deployment patterns, monitoring, alerting, logging, and operational readiness for AI-enabled services.

Technical Workstream Leadership

• Play a lead role in managing the team’s technical work, including work breakdown, prioritization support, sequencing, dependency tracking, and delivery coordination.
• Lead complex data science and AI application workstreams from problem definition through research, experimentation, application development, validation, production readiness, and post-launch improvement.
• Guide technical decisions related to model selection, data strategy, application design, AWS architecture, evaluation design, metrics, quality thresholds, and implementation approach.
• Identify risks, blockers, and trade-offs early and communicate them clearly to engineering, product, and leadership stakeholders.
• Help establish standards, reusable patterns, and best practices for AI/ML delivery, application development, and cloud-based implementation acr