Quantitative Analysis Intern

Group 1001•Stamford, CT

8d•Hybrid

About The Position

We are seeking an intern to join our Quantitative Analysis Department for FALL 2026. Primary responsibilities for this position include performing research and quantitative analysis to support product development. This individual must have excellent analytical capability, strong collaboration and communication skills as well as the ability to deliver high quality work with strong attention to detail. This individual will report and work closely with RVI's Quantitative Analysis Team. This is a full-time, 40-hour-per-week internship following a hybrid schedule, with two days required in-office each week.

Requirements

Enrolled in a Graduate Degree program in actuarial science, statistics, economics, financial engineering, or other related fields
Minimum of one year completed in current Graduate program
0-6 months experience in an analytical position
Proficient in Microsoft Excel and working knowledge of Stata
Proficient in SQL
Proficient in Python, R or other statistical software
Excellent skills and/or experience in data analysis and management
Excellent grammar, written and spoken communication skills in English
Ability to work on assigned research topic and achieve results independently
Must have excellent communication skills
Strong interpersonal and collaboration skills
Ability to learn new tasks and skills quickly; comfortable asking questions and verifying information and requirements

Nice To Haves

Knowledge/ experience in auto industry preferred
Knowledge/ experience in insurance industry preferred

Responsibilities

Collect and process external data to support current market value, residual value forecast assessment, vehicle identification process, and assigned research topic
Analyze historical data and provide recommendations to enhance forecast processes
Conduct research on internship topic
Present research and findings of market trends and forecast processes
Data Analysis for LVS: analyze datasets to identify behavioral patterns in used car ownership and derive operational limits from the data.
Investigate patterns in the dataset showing consumer behavior in used car ownership
Identify practical limits and constraints observable from the data
Check assumption from new-to-used ownership with a detailed distribution of ownership lag years
Provide recommendations for incorporating findings into underwriting assumptions.
Risk Management: Loss Tracing Data Analysis: work on developing a loss tracing tool and performing deep-dive analyses of loss-related datasets.
Build a tool to trace losses through the claims process
Analyze loss data, remarketing data, and behavioral data to identify patterns and root causes
Extract insights into claim process efficiency and potential pain points
Recommend actions to prepare for future risk mitigation strategies.
Operational Data Loading and Processing Automation: manage monthly loading and preprocessing of external datasets.
Handle monthly data loading
Standardize and validate datasets for consistency and completeness
Develop simple automation scripts to reduce manual effort
Document the workflow for smooth handover and repeatability.
CE Data Science Project: lead the CE data analysis project, focusing on uncovering relationships within the data to improve process efficiency and outcome quality.
Identify patterns and correlations within CE datasets to inform decision-making
Develop analytical frameworks that guide process improvements
Summarize insights to support a more efficient and data-driven process.