Quantitative Analyst Intern

About the role

Rho is a modern banking platform designed specifically for startups, offering account opening, card issuance, expense management, bill payment, and bookkeeping in a single connected system supported by real human assistance. As a Quantitative Analyst Intern, you will contribute to high-impact data initiatives that help Rho understand and predict customer behavior across the platform. Your work will focus on identifying early warning signs of account churn, uncovering growth opportunities, and building predictive models that drive customer retention and expansion. You will take full ownership of your analyses and present findings to both technical and non-technical stakeholders, working closely with the growth engineering team that powers expansion, retention, and churn mitigation efforts.

Key facts

What you'll do

1 Design and run experiments to detect early indicators of customer account churn and treasury drawdown patterns before they become costly

2 Build predictive models that identify accounts likely to increase deposits, hire new staff, raise funding, or expand their treasury on Rho

3 Extract meaningful signals from unstructured data sources such as call transcripts using large language model extraction techniques

4 Develop probabilistic models that replace hand-tuned rules with measured weights and quantify how different signals interact with one another

5 Map shared-investor and vendor co-occurrence structures to detect fundraising contagion patterns and identify referral clusters

6 Improve customer health modeling by analyzing how accounts transition between health states and what predicts those transitions

7 Evaluate post-sales team playbook effectiveness and identify areas where response strategies could be strengthened

8 Find and remove friction in go-to-market workflows to improve how signals reach the field teams

9 Analyze product adoption patterns to identify behaviors that predict or drive adoption and increase customer retention

10 Cluster accounts into behavioral archetypes based on quantitative signal combinations and measured weights

11 Backtest candidate churn and expansion indicators against known historical outcomes and graduate the most reliable predictors

12 Work with large, complex datasets that are messy and incomplete, running a high volume of experiments to find actionable insights

Requirements

1 Currently enrolled in a challenging coursework program in Computer Science, Mathematics, Statistics, Data Science, or a closely related quantitative field

2 Demonstrated project experience in statistics, machine learning, econometrics, or a related quantitative discipline

3 Proficient in Python programming language for data analysis and modeling tasks

4 Comfortable working with SQL to query and manipulate large datasets

5 Willingness to run a high volume of experiments and work with messy, incomplete, and imperfect data

6 Ability to communicate quantitative work clearly to both technical and non-technical stakeholders

7 Strong attention to detail and a deeply analytical mindset that reasons from data and quantifies claims

8 High throughput orientation, preferring to run multiple experiments per week rather than waiting for a single perfect result

Nice to have

1 Comfort with ambiguity and the ability to work on problems that arrive underspecified or without clear instructions

2 A take-ownership mentality where you run experiments end to end without needing handholding or constant guidance

3 A habit of questioning everything, including your own numbers, signals, and assumptions, before trusting any result

4 A very fast learning ability that allows you to pick up new tools, data sources, and methods quickly and independently

Skills & tools

1 Python programming for statistical modeling, machine learning, and data analysis workflows

2 SQL for querying, joining, and manipulating large relational databases

3 Large language models for extracting structured signals from unstructured text data such as call transcripts

4 Probabilistic modeling frameworks for quantifying signal interactions and measuring the importance of different variable combinations

5 Graph and network analysis techniques for mapping shared-investor relationships and vendor co-occurrence structures

6 Experiment design and backtesting methodologies for validating predictive indicators against historical outcomes

Practical notes

1 This is an in-person internship based in New York City, requiring on-site presence during working hours

2 The position starts as soon as possible, so candidates should be available to begin immediately

3 You will collaborate across multiple teams including growth engineering, post-sales, and product to drive workflow efficiency

4 The role involves working with untouched data sources such as call transcripts that have not yet been analyzed for signal extraction

5 You will be expected to communicate findings clearly to audiences with varying levels of technical expertise

6 This is a paid internship with an hourly rate of $20 to $35 depending on experience

7 Candidates should expect to work independently and take initiative in a fast-paced environment with minimal supervision

About the company

Rho is the modern banking platform built for startups. Open accounts in minutes, issue cards, manage expenses, pay bills, and close the books - all in one connected platform backed by real human support.