Director, Data Science: Data Science Tools

Design and build internal tools, pipelines, and applications that improve model development, evaluation, and deployment Own strategy and roadmaps for improving data science workflows and tooling across USRM Design, build, and maintain Python packages used across the organization Evaluate and implement AI agent capabilities in tooling using approaches such as MCP, RAG, PydanticAI, Lang Chain, or related frameworks Work with workflow and modeling tools such as Luigi, Airflow, Celery, MLflow, H2O, scikit-learn, Optuna, and LightGBM, as well as Python development tools such as Pydantic, FastAPI, uv, ruff, and pytest Promote MLOps and AI agent best practices in collaboration with groups such as Enterprise Data & Data Science Stay current on developments in open-source data science frameworks, MLOps, and agentic coding practices Help shape the direction of the Tools team and contribute to a culture of ownership, collaboration, and continuous improvement Worked with any of the following in a professional setting: Git, Bash/shell scripting, uv, pre-commit, ruff, pytest, or Pydantic Implemented or supported AI/LLM-based tooling using frameworks such as PydanticAI, Lang Chain, MCP, or RAG Developed, reviewed, or maintained internal Python packages, APIs, or data science applications using tools such as FastAPI, Streamlit, Dash, NiceGUI, or Plotly Broad knowledge of predictive analytic techniques and statistical diagnostics of models. Advanced knowledge of predictive toolset; reflects as expert resource for tool development. Demonstrated ability to exchange ideas and convey complex information clearly and concisely. Ability to establish and build relationships within and outside the organization. Ability to give effective training and presentations to management and other groups. Ability to use results of analysis to persuade team, department management or senior management to a particular course of action. Broad knowledge of business drivers and market context. Has a value driven perspective with regard to understanding of work context and impact. Competencies typically acquired through a Ph.D. degree (in Statistics, Mathematics, Economics, Actuarial Science or other scientific field of study) and a minimum of 3 years of relevant experience, a Masters degree (scientific field of study) and a minimum of 6 years of relevant experience or may be acquired through a Bachelors degree (scientific field of study) and a minimum of 8 years of relevant experience.