Sr Statistical Modeler

About the Role

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

The team is AI forward, suited for a practitioner with hands‑on experience building, operationalizing, and integrating models into production systems. The role collaborates closely with engineering, product, and platform teams and communicates analytical insights to both technical and non‑technical stakeholders.

• Apply and integrate statistical, mathematical, predictive modeling, and business analysis skills to manage and manipulate complex data from a variety of sources.
• Develop and maintain infrastructure systems that connect internal data sets; create new data collection frameworks for structured or unstructured data.
• Serve as a recognized expert within the function, requiring specialized depth and/or breadth of expertise.
• Interpret internal or external business issues and recommend best practices.
• Work independently, with guidance only in the most complex situations.
• Train and mentor junior staff.
• Serve as an expert of own discipline to clients.
• Interpret business challenges and recommend best practices to improve products, processes, or services.

• Proven data science experience; advanced academic experience such as a Master’s degree in a related discipline may substitute for part of the required experience.
• Solid experience 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 (pandas, Num Py, 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 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 a defined scope.
• Clear and effective communication skills, able to explain technical ideas in a way that’s easy for non‑technical audiences to understand.

US National Base Pay Range: $104,900 - $174,700. Geographic differentials may apply. The role is eligible for an annual incentive bonus.

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.