Data Scientist I

Tavern Research•Chicago, IL

36d•Onsite

About The Position

At Tavern Research, weâre building models to understand and influence how people form opinions online. That means taking on problems like tracing how narratives spread through social platforms, or analyzing how different messages land with different audiences. The data is messy, the questions are ambiguous, and the stakes are high. As a member of the modeling team, your role is to help make this work possible. Youâll dive into raw, noisy datasets to clean, structure, and prepare them for analysis. Youâll run exploratory analyses to surface patterns, build features, and support experiments. And youâll contribute to well-defined modeling tasks, helping turn difficult, open-ended questions into tangible, testable results. From day one, youâll be hands-on with real-world data and problems that matter. Youâll have support from senior team members, but youâll also be expected to bring rigor, patience, and persistence to your work. Over time, as your skills deepen, youâll have opportunities to take ownership of increasingly complex modeling projects, building toward the point where you can design and lead experiments yourself. We value in-person collaboration and expect employees to work regularly from our Chicago office.

Requirements

This role is designed for early-career candidates with 0â3 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.
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 youâve done (not just âI ran the codeâ).
Strong communication skills and a collaborative mindsetHumility, patience, and attention to detail.

Nice To Haves

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.