Sr Statistical Modeler

Role Description

The Sr Statistical Modeler 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.

Responsibilities

• Applies and integrates statistical, mathematical, predictive modeling, and business analysis skills to manage and manipulate complex data from a variety of sources.
• Develops and maintains infrastructure systems that connect internal data sets; creates new data collection frameworks for structured or unstructured data.
• Recognized expert within the function.
• Requires specialized depth and/or breadth of expertise.
• Interprets internal or external business issues and recommends best practices.
• Works independently, with guidance in only the most complex situations.
• Trains/mentors junior staff.
• Serves as an expert of own discipline to clients.
• Interprets internal/external business challenges and recommends best practices to improve products, processes, or services.

Qualifications

• 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 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.

Requirements

• This job is eligible for an annual incentive bonus.
• Application deadline is 07/30/2026.

Benefits

• We are delighted to offer country-specific benefits.
• We are committed to providing a fair and accessible hiring process.