Senior Data Scientist II

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 II develops and implements analytics and AI solutions that support business and product objectives across LexisNexis Risk Solutions. This role independently executes complex analytical work, translating well defined problem statements into scalable machine learning solutions across the full modeling lifecycle.

This position is intentionally AI forward, suited for a practitioner with hands on experience building, operationalizing, and integrating models into production systems. The Senior Data Scientist II collaborates closely with engineering, product, and platform teams and communicates analytical insights to both technical and non technical stakeholders.

Key Responsibilities:

• Design, build, and implement machine learning and statistical models to support analytics and AI use cases.
• Independently execute complex analytical projects within defined scope, including data exploration, feature engineering, modeling, and evaluation.
• Contribute to end‑to‑end AI solutions, supporting data pipelines, model training, inference, and integration with downstream systems.
• Support the development and integration of model inference services and APIs that enable real‑time or near‑real‑time AI workflows.
• Operationalize models in production or near‑production environments, following established engineering and analytics best practices.
• Collaborate with engineering and platform teams on APIs, services, and analytics infrastructure.
• Develop reusable analytical components that support scalability and maintainability.
• Participate in technical design discussions and code reviews, reinforcing team standards and best practices.
• Communicate analytical results, model performance, and limitations to internal stakeholders.
• Support model monitoring, documentation, validation, and ongoing improvement activities.

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 applying machine learning and statistical techniques to real‑world problems.

• Hands‑on experience developing, evaluating, and iterating on predictive and machine learning models.
• Experience evaluating model performance using appropriate statistical and machine learning metrics and validation techniques.
• Experience working with structured and unstructured data at scale.
• Proficiency in Python and/or R using common data science and machine learning libraries (e.g., pandas, NumPy, scikit‑learn, XGBoost, PyTorch).
• Experience working with SQL and relational or cloud‑based data platforms.
• Hands‑on experience developing and running data science and AI workloads in cloud environments such as AWS and Azure, including compute, storage, monitoring, and cost‑aware execution.
• Exposure to modern AI frameworks and tools, including large language model (LLM)–based solutions and retrieval‑augmented workflows.
• Experience training, fine‑tuning, or evaluating neural network–based models as part of applied machine learning solutions.
• Experience in applying software engineering best practices to data science codebases, including testing, code quality checks, and version control workflows.
• Ability to independently execute complex analytical work within defined scope.
• Clear and effective communicator, able to explain technical ideas in a way that’s easy for non‑technical audiences to understand.

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