Predoctoral Researcher

Overview

Professor(s) Ruru Hoong, Anya Shchetkina, and Jimin Nam (MIT Sloan) are seeking motivated and detail-oriented individuals to work as full-time pre‑doctoral researchers. The position involves close collaboration with 1–2 faculty members (depending on research interests) on empirical research projects related to digital technologies, advertising, and AI. You will contribute at all stages of the research process — from data collection and cleaning to analysis and writing.

Some structural modelling or applied ML methods development may be involved if of interest.

• The design of information for human‑AI collaboration
• The impact of generative AI on worker productivity, task allocation, and organizational design
• The design of AI‑driven hiring tools, such as optimizing voice‑AI interviews for candidate screening
• Youth, social media, and smartphones
• The effects of AI on education and learning, including adaptive AI tutoring and path dependence in AI product rollout
• AI and wellbeing, including how generative AI tools affect individual and worker wellbeing
• Household and business surveys on electricity in Ghana
• Mechanization and preferences – marketing in 19th Century tea
• Measuring advertising effectiveness with aggregate data
• Experimental design under privacy constraints
• Identifying limits of targeting and personalization effectiveness

Methods used across these projects include randomized controlled trials, experiments, machine learning, natural language processing, large language models, probabilistic ML, and adaptive learning (bandits and Bayesian optimization).

A core goal of the position is to prepare you to apply to and succeed in PhD programs in marketing, economics, management, operations or related fields. We will work together to develop your research skills — beginning with careful research workflow and attention to detail, and progressing toward more independent analysis over time. We will also collaborate on other elements of your preparation, including independent research, letters of recommendation, and coursework planning.

You will be encouraged to attend seminars and engage with the broader research community at MIT Sloan and across MIT.

• Collect, clean, and manage large‑scale quantitative and qualitative datasets across active research projects related to marketing, digital economics, and AI
• Conduct empirical analysis using methods such as causal inference, machine learning, NLP, and experimental design
• Contribute to all stages of the research process — from literature review and data collection through analysis and writing
• Design and implement surveys and experiments using tools such as Qualtrics and oTree
• Write, review, and maintain reproducible research code in Python, R, or Stata, including responsible use of AI coding tools
• Other duties as needed

• A Bachelor’s degree with strong grades, ideally in a quantitative field
• Comfort with or eagerness to learn programming (Python, R, or Stata) and experimental tools (Qualtrics, oTree)
• Willingness to experiment with AI coding tools (Claude, Cursor, Copilot) while maintaining a healthy skepticism — you should be prepared to understand and review every line of code these tools generate
• A long‑term interest in pursuing a PhD in marketing, economics, management, operations, or a related field
• A familiarity with causal inference or Bayesian statistics is welcome but not required
• Prior research experience is a plus but not a prerequisite
• Careful attention to detail — the kind of person who double‑checks a merge and notices when a number doesn’t look right
• Curiosity, initiative, and the persistence to work through open‑ended problems
• The ability to work independently
• Visa sponsoring will not be available for this position. Candidates should have authorization to work in the US for the duration of the appointment

The position is located at the MIT Sloan School of Management in Cambridge, Massachusetts. The default start date is July 1, 2026, with some flexibility to begin earlier or later. The position typically lasts 1–2…