Quantitative Analyst Intern, Rho

• Challenging coursework in Computer Science, Mathematics, Statistics, Data Science, or a related quantitative field
• Project experience in statistics, ML, econometrics, or a related quantitative field
• Proficient in Python, Comfortable with SQL
• Willing to run a high volume of experiments and work with messy, incomplete data
• Communicates quantitative work clearly to technical and non-technical stakeholders

Desirable

• You'll work on high-impact data projects that help Rho detect, prevent, and better understand customer behavior, from identifying early churn signals to mapping growth opportunities
• Your work will directly support Rho's growth engineering function, the systems that power expansion, retention, and churn mitigation
• The work spans designing experiments, building predictive models, extracting insights from unstructured data, and working with large, complex datasets
• You'll collaborate across teams to drive workflow efficiency, improve customer retention, and influence product direction, taking full ownership of your analyses and communicating your findings clearly to technical and non-technical audiences
• New churn leading indicators. The current set catches a lot, but not everything. Hunt for earlier, cleaner predictors of account churn and treasury drawdown. Backtest candidates against known outcomes and graduate what holds
• New expansion and deposit-growth signals. The upside side of the book is less built out than the churn side. What predicts an account is about to move more money onto Rho, hire, raise, or grow its treasury? Generate and test candidates
• Unstructured data as a new signal source. Today's entire signal universe lives in the warehouse; call transcripts are untouched. Build LLM extraction experiments to pull signals that will never appear in transaction data: a competitor mentioned on a call, product-limit frustration, expansion intent voiced directly. A different modality, genuinely additive
• Probabilistic modeling. Move signal scoring from hand-tuned rules toward measured weights. Model how signals interact, quantify which combinations actually matter, and cluster accounts into behavioral archetypes
• Graph and network signals. Map shared-investor and vendor co-occurrence structure for fundraise-contagion detection and referral clusters. Untouched today
• Customer health modeling. Improve how we model account health over time: how accounts move between health states, what predicts those transitions, and where intervention changes the trajectory
• Playbook effectiveness. Analyze how post-sales teams respond to churn signals and identify where playbooks could improve
• GTM workflow efficiency. Find and remove friction in how signals reach the field
• Product adoption. Analyze adoption patterns, identify behaviors that predict or drive adoption, and use them to make customers stickier