Senior Data Scientist III

Do you thrive in senior, hands‑on data science roles where you apply deep healthcare domain expertise, influence technical decisions, and translate advanced models into real‑world impact?

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 the link below.

https://risk.lexisnexis.com/insurance

About the Team:

You’ll join a collaborative, high‑impact analytics team that partners closely with product, engineering, and business leaders to turn complex data into trusted, production‑ready insights that drive smarter decisions across the insurance lifecycle.

About the Role:

The Senior Data Scientist III is a senior individual contributor role at LexisNexis Risk Solutions, responsible for driving complex healthcare analytics initiatives and delivering advanced statistical, machine learning, and AI solutions that support product innovation and business outcomes. The role spans the full analytics lifecycle—from problem framing and research through model development, validation, deployment, and stakeholder communication.

The SDIII applies deep healthcare domain knowledge and strong analytical rigor to translate complex healthcare and insurance data into scalable, production‑ready models and insights. The role partners closely with product management, engineering, data/platform teams, and business stakeholders to provide technical leadership across healthcare analytics initiatives.

Key Responsibilities:

• Drive the design, development, validation, and deployment of advanced statistical, machine learning, and AI models supporting healthcare and insurance products.
• Apply deep healthcare analytics expertise to work with complex data sources such as medical claims, eligibility, provider, and pharmacy data, including standard coding systems (e.g., ICD‑10, CPT, HCPCS, DRGs, RxNorm, NDC).
• Conduct advanced exploratory analysis and experimentation to assess feasibility, model performance, bias, and business impact of analytics solutions.
• Develop and apply modern machine learning techniques, including NLP and selective use of generative AI approaches, to healthcare analytics problems where appropriate and responsible.
• Provide senior‑level technical guidance on modeling approaches, feature engineering, validation strategies, and coding practices to ensure analytical rigor, robustness, and regulatory awareness.
• Identify, document, and manage analytical and model risks, including data limitations, fairness considerations, explainability, and downstream usage implications in healthcare contexts.
• Partner with product managers, business leaders, operations, IT, and client‑facing teams to translate healthcare analytics solutions from concept to production.
• Communicate complex analytical findings, trade‑offs, and recommendations clearly to both technical and non‑technical audiences.
• Contribute to the continuous improvement of healthcare analytics standards, tools, documentation, and best practices across the organization.
• Maintain thorough documentation of data sources, assumptions, methodologies, models, and code to support transparency, auditability, and reuse.
• Stay current with evolving healthcare data standards, analytics methodologies, and AI/ML trends relevant to LNRS products and clients.

Requirements:

• Proven Data Science experience, Advanced academic experience—such as a Master’s degree or Doctoral degree in a related discipline—may substitute for part of the required experience

• Solid experience in statistical modeling, machine learning, and advanced analytics, including experience deploying models into production environments.
• Demonstrated experience working with healthcare and/or insurance data, with a strong understanding of medical claims and healthcare coding systems.
• Proficiency in Python and common data science and machine learning libraries (e.g., pandas, NumPy, scikit‑learn, XGBoost, PyTorch).
• Experience applying NLP techniques and familiarity with generative AI concepts (e.g., LLMs, prompt engineering, retrieval‑augmented generation) in applied analytics settings.
• Experience working with large, complex datasets