Data Analyst — Economic & Legal Analytics (Hybrid)

Cornerstone Research; New York (NY); Hybrid; USD 75,000 - 115,000; Full time

Job summary

Cornerstone Research is seeking a Data Analyst to support rigorous analyses for complex litigation, regulatory investigations, and financial disputes. You will join case teams with experts and senior consultants to contribute from data ingestion through final deliverables.

The role is open to multiple U.S. offices and operates in a hybrid model requiring at least 50% in-office collaboration, with emphasis on building large datasets, models, and reproducible outputs for client-facing work.

Qualifications

• BA/BS or MA/MS in a quantitative field with strong academic performance
• 1–3 years of relevant work experience with data analysis, econometrics, statistics or programming
• Strong ability to manipulate structured datasets and produce clean outputs
• Working knowledge of SQL for data extraction and analysis
• Proficiency in Python or R and familiarity with Bash/CLI/Git

Responsibilities

• Execute exploratory and econometric analyses on case data to accelerate insight
• Own end-to-end data workflows including ingestion, cleaning, transformation, and validation
• Build, document, and maintain data infrastructure and pipelines with reproducibility
• Translate case strategy into structured, reproducible data outputs
• Uphold coding standards and serve as technical reference for data decisions
• Lead escalation and resolution of technical issues on data work

Skills

SQL

Python

R

Bash/CLI/Git

Data analysis

Statistics

Education

BA/BS or MA/MS in quantitative field

Tools

Redshift

S3

Databricks

SQL Database

Job description

Cornerstone Research is seeking a Data Analyst to support rigorous analyses for complex litigation, regulatory investigations, and financial disputes. You will join case teams with experts and senior consultants to contribute from data ingestion through final deliverables.

The role is open to multiple U.S. offices and operates in a hybrid model requiring at least 50% in-office collaboration, with emphasis on building large datasets, models, and reproducible outputs for client-facing work.