AI and Market Outcomes (Flexible Research Position)
J. Miguel Villas-Boas, Professor
Business, Haas School
Open. Apprentices needed for the fall semester. Enter your application online beginning August 21st. The deadline to apply is Monday, August 31st, 4pm.
We are looking for an undergraduate student to provide flexible research support across 2 early-stage AI and quantitative marketing projects at Berkeley Haas.
This is an ad-hoc, execution-focused role rather than a primary research position. Tasks will depend on the projects’ changing needs. Some weeks may require approximately 6-8 hours of work, while other weeks may have little or no assigned work. Meetings will generally occur weekly or biweekly.
Application process: Applicants should submit their application through the URAP portal and have weekly availability. If available, include a GitHub profile or example of previous technical work in the application.
Selected applicants will complete a small research exercise, submit a resume, and attend a short interview. The exercise will emphasize reasoning, communication, and willingness to learn.
Role: Role (key tasks)
- Data cleaning, organization, labeling, and quality checks.
- Reviewing machine-generated outputs.
- Running existing scripts or model experiments.
- Conducting focused literature searches.
- Organizing research files, results, and documentation.
- Assisting with other short-term research needs.
- Work with other research assistants and support each other on AI pipeline building.
Some assignments may be routine but important. The student should be comfortable completing necessary support work carefully and reliably, even when the task is not technically complex. Students are expected to pay for their own computing and storage services. Due to the long publication cycle of business / economic projects, most of our URAP programs don't grant authorship.
Expectations
- Responsible, responsive, and attentive to detail.
- Able to follow instructions and meet agreed deadlines.
- Basic proficiency in Python or data analysis.
- Comfortable learning unfamiliar tools independently.
- Willing to ask questions when instructions or results are unclear.
- Able to understand, verify, and explain any AI-assisted work submitted. Use of ChatGPT and coding assistants is welcome, but copying outputs without checking or understanding them is not.
What You’ll Gain
- Exposure to early-stage AI and quantitative research.
- Practical experience with research data, AI-generated outputs, and reproducible workflows.
- Familiarity with how academic projects develop and change over time.
- Occasional feedback and guidance during weekly or biweekly meetings.
Because this is a limited-hours support role, it will involve less direct mentorship and project ownership.
Qualifications: Qualifications (required)
- Strong responsibility, attention to detail, and clear communication
- Coding in Python (e.g., Data 100 or similar), plus solid data cleaning skills
- Self-motivated and comfortable learning quickly (including using ChatGPT effectively)
Preferred
- ML/LLM exposure (e.g., CS 189 or related AI/ML/NLP coursework and projects)
- Prior experience with fine-tuning.
- Experience with Git, PyTorch, Hugging Face, model APIs, or data-processing pipelines.
Day-to-day supervisor for this project: Debbie Liang, Graduate Student
Hours: 6-8 hrs
Off-Campus Research Site: or virtual.
Social Sciences Digital Humanities and Data Science