Quantitative Researcher

As a Quantitative Researcher at Tavern Research, you will be responsible for analyzing survey data, writing insightful reports on survey findings and randomized trials, and organizing data to enhance measurement and prediction. You will build models that inform our strategies and research innovative techniques to constantly improve our analytical work.

Tavern Research looks for candidates who have expertise in scientific Python, experience with survey data, and some familiarity with causal inference. Importantly, candidates do not need a formal degree; what matters is your proficiency in the required skills and an eagerness to learn.

At Tavern Research, the work environment is dynamic and collaborative. The team meets in our Chicago office three days a week, fostering a culture of transparency and knowledge sharing. We embrace curiosity and adaptability, supporting each other in a fast-paced setting where skilled insights lead the way.

Tavern Research values continuous learning and supports your growth by allowing you to tackle significant problems and access the latest tools in the industry. You will have opportunities to refine your analytical skills while collaborating with team members to enhance your expertise in quantitative research.

Tavern Research provides a comprehensive benefits package that includes premium health insurance, unlimited PTO, and a 401k match. We also celebrate diversity and work to create an inclusive environment where all our employees can thrive.

When answering this question, share specific projects where you utilized Python for data analysis. Highlight your proficiency with libraries like Pandas, NumPy, and any specific techniques you used to manipulate or analyze data.

Discuss your methodology for approaching survey data analysis. You might explain how you clean the data, handle missing values, and interpret results to extract actionable insights. Be sure to mention any relevant software or statistical methods you employ.

Illustrate your understanding of causal inference by describing any techniques you have applied, such as propensity score matching or regression discontinuity designs. Be ready to provide examples from your experience where these techniques led to significant outcomes.

Share a specific project that posed challenges and describe how you approached it. Emphasize your role and contributions, highlighting teamwork, decision-making processes, and how you delivered results amid difficulties.

Addressing uncertainty is crucial in quantitative research. Explain your approach to communicating uncertainty to clients and colleagues, and how you incorporate it into your analyses, ensuring stakeholders understand the implications.

Discuss various methods such as feature engineering, regularization techniques, or ensemble methods that can enhance model accuracy. Share any experiences where applying these techniques helped improve your results.

Mention specific journals, conferences, or online courses you follow or participate in to stay informed. Highlight how this ongoing learning has positively influenced your work and overall understanding of the field.

Explain your belief in the importance of collaboration, sharing examples of how working with others—whether in brainstorming sessions or peer review of analyses—leads to more robust findings and a richer understanding of data.

Prepare to discuss an instance where you simplified complex data for a broader audience. Describe how you tailored your communication style and which tools or visuals helped convey your message effectively.

Your response should reflect your passion for the political tech field and alignment with Tavern Research's mission. Express how your skills and values resonate with the company, and why you are excited about the opportunity to contribute to their goals.