Research Intern - Statistics & Financial Advisor Insights

Research Intern – Statistics & Financial Advisor Insights

• Location: Remote (US-based)
• Commitment: Part-time (5–20 hours per week)
• Compensation: $40/hour

About Jump

Jump is the leading AI assistant for financial advisors (we raised our $20 million Series A from Battery Ventures in January), and one of our major focus areas is identifying advisor best practices by doing research on hundreds of thousands of aggregated, anonymized advisor <> client conversations.

Role Overview

To help with this, we’re looking for a research intern with training in observational causal inference and causal machine learning who’s excited to apply their skills to real-world problems in financial advising. This person’s primary role will be to help us ensure that our methodology is as rigorous and reliable as possible. You’ll work alongside our Director of Insights, Liam Hanlon, and the broader research team to:

• Apply observational causal inference methods with clear identification strategies to isolate conversational variables that causally influence outcomes.
• Engineer structured features from unstructured transcript data (e.g., advisor talk ratio, sentiment, interruptions, trust markers, hesitations) using LLMs, embeddings, and NLP.
• Analyze large-scale anonymized transcript datasets.
• Strengthen the methodological rigor of our research design and analysis.
• Contribute to research that pushes the financial advising industry forward.
• Develop a sustainable process and reusable causal model that the team can operate independently after the internship, ensuring continuity and scalability of insights.

This role is highly flexible and designed for students looking to gain applied research experience while pursuing their degree.

What You’Ll Work On

• Building statistical and causal models to assess advisor effectiveness.
• Designing and applying robust causal inference strategies to observational data.
• Exploring causal ML approaches to uncover behavioral drivers of outcomes.
• Developing and documenting best practices to ensure methodological rigor.
• Summarizing findings for both technical and non-technical audiences.
• Collaborating with a cross-functional team of researchers, engineers, and product leaders.

What We’Re Looking For

• Graduate student (MA/PhD) or college senior in statistics. (Highly qualified juniors are also eligible, and we’re also open to those in applied math, computer science, or quantitative economics with applicable training.)
• Training in observational causal inference and causal machine learning.
• Strong foundation in statistical modeling and data analysis.
• Curiosity and exploratory creativity: the ability to go beyond validating predefined hypotheses and propose / uncover novel conversational levers.
• Experience working with large datasets (Python, R, or similar).
• Familiarity with NLP or interest in applying LLMs to real-world research problems.
• Intellectual curiosity and a passion for using data to drive impact.
• Commitment to methodological rigor and careful research design.
• Bonus: Familiarity with behavioral science, financial services and causal ML libraries such as EconML, DoWhy, or CausalNex.

Why Jump

• $40/hour for part-time work (5–15 hours per week).
• Work with one of the most unique and rich datasets in all of financial services
• Apply cutting-edge methods to questions that have never been answered before in this industry
• Flexible, remote-friendly work environment.
• Hands-on research experience with a unique dataset and cutting-edge methods.
• Opportunity to publish, share, and apply your work in an industry with real-world stakes.

For examples of our research, check out recent posts from our Director of Insights, Liam Hanlon.

Salary: 40 USD an hour