Description
The Sr Statistical Modeler develops and implements analytics and AI solutions that support business and product objectives across LexisNexis Risk Solutions.
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
Requirements:
- 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.
The U.S. National Base Pay Range is $104,900 - $174,700. Geographic differentials may apply in some locations.