Sr Statistical Modeler
About The Position
The Sr Statistical Modeler develops and implements analytics and AI solutions that support business and product objectives across LexisNexis Risk Solutions. The role independently executes complex analytical work, translating well-defined problem statements into scalable machine learning solutions across the full modeling lifecycle. The team is intentionally AI-forward and works closely with engineering, product, and platform teams. The role requires hands-on experience building, operationalizing, and integrating models into production systems, as well as communicating analytical insights to technical and non-technical stakeholders. LexisNexis Risk Solutions’ Business Services vertical provides solutions that help businesses improve revenue growth, operational efficiency, and customer experience, including offerings in anti-money laundering and counter-terrorist financing, identity authentication and verification, fraud and credit risk mitigation, and customer data management.
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 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 with appropriate statistical and machine learning metrics and validation techniques.
- Experience working with structured and unstructured data at scale.
- Proficiency in Python and/or R and common data science and machine learning libraries, such as pandas, NumPy, scikit-learn, XGBoost, and 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.
- 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, including the ability to explain technical ideas to non-technical audiences.
Nice To Haves
- 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.
Responsibilities
- 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, and create data collection frameworks for structured or unstructured data.
- Interpret internal or external business issues and recommend best practices.
- Work independently, seeking guidance only in the most complex situations.
- Train and mentor junior staff.
- Serve as a discipline expert for clients.
- Recommend best practices to improve products, processes, or services.
Benefits
- Annual incentive bonus
- Country-specific benefits; details vary by location
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
