Quantitative Analysis Intern

Group 1001 is a consumer‑centric, technology‑driven family of insurance companies on a mission to deliver outstanding value and operational performance by combining financial strength and stability with deep insurance expertise and a can‑do culture. Group
1001’s culture emphasizes the importance of collaboration, communication, core business focus, risk management, and striving for outcomes. This goal extends to how we hire and onboard our most valuable assets – our employees.

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.

• 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

To support underwriting and market insight generation, the position will 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.

The topic will require statistical analysis, data visualization, and a final presentation on insights for underwriting use.

To support risk management and mitigation planning, the position will 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.

The topic will require tool development, data analysis, and a final presentation of findings and recommendations.

To support data operations for PV and related functions, the position will 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.

The topic will require basic data processing skills, attention to detail, and operational documentation.

To provide CE with more actionable data for making better and more efficient decisions, the position will 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.

The topic will require exploratory analysis, and a final presentation of findings and actionable recommendations.

• 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
• Knowledge/ experience in auto industry preferred
• K…