Assistant Director Data Science

Are you curious, resilient, independent, and always interested in learning and working on state-of-the-art modeling techniques, applying the best science to deliver business value? The USDS Data Science Excellence team is looking for an Assistant Director to research, design, and build major modeling innovations and pioneer model sophistication across our retail insurance products.

Key Responsibilities:
- Design, develop, and scale gradient-boosted decision-tree models end-to-end: problem framing, data wrangling, exploratory data analysis (EDA), feature engineering, hyperparameter optimization, and model evaluation.
- Build and optimize large-scale training workflows (e.g., parallel/distributed training on AWS EMR or similar environments).
- Research and experiment with new modeling techniques for structured and unstructured data, and evaluate emerging ML tools and best practices.
- Own pioneering projects from research through proof of value and product ownership; identify and remove obstacles to deployment in partnership with infrastructure and design teams.
- Research, design, and assist with the implementation of models in Earnix and other platforms.
- Adopt a transformational mindset to improve and automate processes where applicable.
- Regularly engage with the data science community and participate in cross-functional working groups.

Skills and Experience:
- Experience building gradient-boosted decision trees and performing model interrogation.
- Strong coding experience; fluent in object-oriented programming, Python, and common Python data and modeling packages (LightGBM, Optuna, SHAP).
- Good software practices: version control (Git), pre-commits, code reviews, documentation.
- Experience with EDA and data preparation for building gradient-boosted decision trees.
- Knowledge of Earnix or similar rating tools is a plus.
- Demonstrated ability to exchange ideas and convey complex information clearly and concisely.
- A value-driven perspective on work context and impact.
- Demonstrated professional-level proficiency in English, with excellent written and verbal communication skills.

Education and experience requirements:
- 8 years of experience in data science
- PhD (in a quantitative field) + 2+ years relevant experience, or
- Master’s + 3+ years relevant experience, or
- Bachelor’s + 5+ years relevant experience.