Part-time Intern (15-20 hours per week)

Location: New York, NY (onsite, hybrid, or remote)
Commitment: 15–20 hours per week

About the Role

We are seeking a highly motivated PhD candidate in Finance to join our research team as an Off-Cycle Intern. This part-time role (15–20 hours per week) is designed for advanced doctoral students who are passionate about empirical finance, with a particular focus on global equities. The position offers the opportunity to work alongside experienced professionals, contribute to ongoing research initiatives, and apply academic expertise to practical investment challenges.

Key Responsibilities

• Conduct empirical research on global equity markets using large-scale datasets (e.g., CRSP, FactSet, accounting, holdings, analyst forecasts, and other academically recognized sources).
• Explore and analyze both traditional and behavioral equity return patterns.
• Apply advanced econometric and statistical techniques to investment-relevant questions.
• Develop, test, and refine quantitative models in Python.
• Communicate findings clearly through written reports and verbal presentations to both technical and non-technical audiences.
• Collaborate with researchers and portfolio managers on projects with direct application to investment strategies.

Requirements

Education and Qualifications

• PhD candidate in Finance (3rd year or beyond; exceptional earlier-stage candidates considered with prior relevant work experience).

Experience

• Significant experience conducting empirical research in global equities using broad universe datasets.
• Strong knowledge of equity market behavior, including both traditional and behavioral return patterns.

Technical skills:

• 4+ years of Python experience, with proficiency in Pandas, NumPy, and scikit-learn.
• Completed graduate-level coursework in econometrics and statistics.
• Excellent written and verbal communication skills.
• Experience with industry risk models (e.g., Barra, Axioma) a plus.
• Based in the New York area; role can be onsite, hybrid, or remote.