Sr Statistical Modeler

Role overview Build and operationalize statistical and machine learning solutions that support product and business goals. You’ll take complex analytical problems from data exploration through model evaluation and production integration, working with engineering, product, and platform teams and explaining findings to both technical and nontechnical audiences.

Responsibilities - Analyze complex data from multiple sources using statistical, mathematical, predictive modeling, and business analysis methods. - Develop, evaluate, and improve predictive and machine learning models, including neural network approaches where appropriate. - Build and maintain systems that connect internal datasets and support collection of structured or unstructured data. - Help integrate models into production systems and contribute to scalable AI and analytics infrastructure. - Recommend improvements to products, processes, and services based on business challenges and analytical findings. - Share expertise with clients and colleagues, including mentoring junior team members.

Requirements - Demonstrated data science experience; advanced study in a related field may substitute for some experience. - Practical experience applying statistical and machine learning methods to real-world problems and validating model performance. - Experience working with structured and unstructured data at scale, SQL, and relational or cloud data platforms. - Proficiency in Python and/or R and common data science libraries. - Experience developing and operating data science workloads in cloud environments, including attention to compute, storage, monitoring, and cost. - Familiarity with LLM-based solutions and retrieval-augmented workflows, plus software engineering practices such as testing, code quality, and version control.

Nice to have Experience training, fine-tuning, or evaluating neural network models and applying modern AI frameworks to practical use cases.