ML - Data Scientist (Statistical Modeling & AI Systems)

Proficient in Python and comfortable writing and querying SQL (BigQuery). Skilled in statistical modeling (e.g., logistic regression, hierarchical/multilevel models, causal inference, treatment effects). Familiar with AI/ML frameworks (e.g., OpenAI, Hugging Face, LangChain) and data science libraries (e.g., scikit-learn, statsmodels, PyMC, or Stan). Comfortable working with large datasets and experimental data pipelines. Clear communicator: able to translate technical results into actionable insights. Collaborative, curious, and impact-driven—you want your code to make a difference.

Develop and scale LLM-based tools that analyze news, social media, and message performance, combining hierarchical Bayesian models with cutting-edge LLM tooling. Build content-generation and summarization systems that interface with campaign data and modeling outputs. Automate and optimize data labeling, model fine-tuning, and model evaluation loops using Python-based frameworks. Design, train, and interpret large logistic regression and hierarchical models to estimate causal effects of political messages and ads. Build pipelines that integrate experimental and observational data for treatment effect estimation. Communicate results and uncertainty clearly to both technical and strategic audiences. Collaborate on A/B testing frameworks and survey experiment design. Architect and maintain modeling and AI infrastructure with clear separation of concerns, reproducibility, and scalability. Write clean, efficient, and well-tested Python code; manage data flow and model deployment. Develop internal APIs and lightweight web tools for data visualization, LLM interactions, and model monitoring. Build and maintain ETL workflows, SQL-based schemas, and workflow automation. Integrate voter files, survey data, and ad-performance metrics into unified modeling pipelines. Support data reliability, performance monitoring, and quality assurance.