Tavern Research is building models to understand and influence how people form opinions online, facing challenges with messy data and ambiguous questions in high-stakes political campaigns.
Requirements
- Experience building statistical models (beyond differential equations or simulations).
- Demonstrated experience working with messy, real-world datasets.
- Proficiency in Python and standard data science libraries.
- Familiarity with AI and machine learning concepts like embeddings, supervised and unsupervised ML methods, and agentic systems.
- Clear understanding of the data science concepts behind work you’ve done (not just “I ran the code”).
- Bonus: exposure to model deployment, large language models, or causal inference.
Responsibilities
- Work hands-on with real-world, unstructured data: cleaning, debugging, and engineering features that support effective models.
- Support the design and execution of numerical experiments by handling data prep, fitting models, and running standard analyses.
- Contribute to feature engineering and basic model building under the guidance of senior modelers.
- Write clean, well-documented Python code using standard data science libraries.
- Assist with model evaluation, diagnostics, and performance monitoring.
- Collaborate with researchers and engineers to scope and execute tightly defined tasks.
- Learn and apply best practices and emerging tools in machine learning, AI, and causal inference.
Other
- This role is designed for early-career candidates with 0–3 years of relevant experience who are eager to grow into more advanced modeling responsibilities.
- Strong academic or applied background in data science and software engineering or closely related fields.
- Strong communication skills and a collaborative mindset
- Humility, patience, and attention to detail.
- 3 days/week in South Loop office
