Do you thrive in senior, handson data science roles where you apply deep healthcare domain expertise, influence technical decisions, and translate advanced models into realworld 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. () About the Team: Youll join a collaborative, highimpact analytics team that partners closely with product, engineering, and business leaders to turn complex data into trusted, productionready 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 lifecyclefrom 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, productionready 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., ICD10, 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 seniorlevel 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 clientfacing teams to translate healthcare analytics solutions from concept to production. \\+ Communicate complex analytical findings, tradeoffs, and recommendations clearly to both technical and nontechnical 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 experiencesuch as a Masters degree or Doctoral degree in a related disciplinemay 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, scikitlearn, XGBoost, PyTorch). \\+ Experience applying NLP techniques and familiarity with generative AI concepts (e.g., LLMs, prompt engineering, retrievalaugmented generation) in applied analytics settings. \\+ Experience working with large, complex datasets across onpremise and cloudbased data platforms. \\+ Ability to operate independently on ambiguous problems, exercising sound technical judgment and accountability for outcomes. \\+ Effective communicator and collaborator, skilled at partnering and influencing across product, engineering, and business teams. Working for You: We know that your wellbeing and happiness are key to a long and successful career. These are some of the benefits we are delighted to offer: \- Medical Inpatient and Outpatient Insurance: Coverage for your healthcare needs.- Life Assurance Policies: Providing financial security for your loved ones.- Modern Family Benefits: Support for maternity, paternity, and adoption needs.- Long Service Award: Recognition for your dedication and loyalty.- Celebratory Allowance/Gifts: Marking special occasions to celebrate with you.- Flexible Benefits Plan : Offering you wider choice of services and products - Employee Assistance Program : Access support for personal and work-related challenges.- Flexible Working Arrangements: Balance work and personal life effectively.- Access to Learning and Development Resources: Empowering your professional growth. U.S. National Base Pay Range: $115,400 - $192,300. Geographic differentials may apply in some locations to better reflect local market rates. This job is eligible for an annual incentive bonus. We know your well-being and happiness are key to a long and successful career. We are delighted to offer country specific benefits. Click here () to access benefits specific to your location. We are committed to providing a fair and accessible hiring process. If you have a disability or other need that requires accommodation or adjustment, please let us know by completing our Applicant Request Support Form or please contact 1-855-833-5120. Criminals may pose as recruiters asking for money or personal information. We never request money or banking details from job applicants. Learn more about spotting and avoiding scams here. Please read our Candidate . We are an equal opportunity employer: qualified applicants are considered for and treated during employment without regard to race, color, creed, religion, sex, national origin, citizenship status, disability status, protected veteran status, age, marital status, sexual orientation, gender identity, genetic information, or any other characteristic protected by law. USA Job Seekers: EEO Know Your Rights. RELX is a global provider of information-based analytics and decision tools for professional and business customers, enabling them to make better decisions, get better results and be more productive. Our purpose is to benefit society by developing products that help researchers advance scientific knowledge; doctors and nurses improve the lives of patients; lawyers promote the rule of law and achieve justice and fair results for their clients; businesses and governments prevent fraud; consumers access financial services and get fair prices on insurance; and customers learn about markets and complete transactions. Our purpose guides our actions beyond the products that we develop. It defines us as a company. Every day across RELX our employees are inspired to undertake initiatives that make unique contributions to society and the communities in which we operate.

The role requires proven data science experience, with advanced academic experience such as a Master's or Doctoral degree in a related discipline potentially substituting for some experience. Solid experience in statistical modeling, machine learning, and advanced analytics, including deploying models into production, is necessary. Demonstrated experience working with healthcare and/or insurance data, including a strong understanding of medical claims and healthcare coding systems, is essential. Proficiency in Python and common data science and machine learning libraries like pandas, NumPy, scikitlearn, XGBoost, and PyTorch is required. Experience applying NLP techniques and familiarity with generative AI concepts such as LLMs, prompt engineering, and retrieval augmented generation in applied analytics settings is also needed. The ability to work with large, complex datasets across on-premise and cloud-based data platforms is expected.