Experts in Vega’s data science and statistics expert network apply empirical methods to complex datasets. The Vega team has vast experience managing large-scale datasets consisting of both structured and unstructured data. By developing rigorous, client-focused solutions, the Vega team helps our clients achieve superior results.
Statistical Analysis & Sampling
Vega develops and implements empirical analyses to answer our client’s economic and financial questions. We rely on flexible, robust, and defensible statistical techniques to address a wide range of issues including sampling, simulation, survival analysis, and other statistical techniques.
Big Data Management
Vega regularly works with large volumes of data. Our staff and in-house infrastructure have the capabilities to build, maintain, update, and most importantly, leverage extensive data to meet client needs. Our expertise enables us to conduct efficient and informative analytics. Our team is skilled at processing, normalizing, and enriching data. We also have experience maintaining datasets with billions of observations spanning many years.
Web Scraping and Data Collection
Vega researches and collects large amounts of publicly available data. Drawing on our experience and creativity, we perform focused searches to identify and gather relevant and informative data unique to each engagement. Our team uses proprietary tools to scrape and compile custom datasets that address the needs of every client.
Text & Sentiment Analysis
Our team has experience analyzing and interpreting extracted text using sentiment analyses. We are able to build algorithms to classify millions of pages of documents and use proprietary tools to identify, categorize, index, and compile relevant documents based on content. In several engagements, Vega economists have also employed modern sentiment analysis.
Data Visualization
The Vega team creates intuitive, aesthetic, and compelling visualizations. Utilizing our programmatic dexterity, we make complex concepts accessible to all audiences. Our team is also able to streamline revisions and ensure consistency in visualizations by automating the generation process.
Artificial Intelligence (AI) & Machine Learning (ML)
Vega uses cutting edge AI and ML technology to guide data-driven analyses. By using complex state-of-the-art data science methods, we are able to process and analyze large volumes of complex information while reducing subjectivity and error. Our approach allows us to automate complex tasks and customize analysis of large datasets.
Below is a list of example engagements for our Data Science & Statistics practice:
Analysis of Factors Impacting Revenue of Independent Medical Practices: Vega Economics and healthcare industry expert Randy Farber are retained by a information technology services provider in a case centered around a hard drive crash to discuss the factors impacting revenue of independent medical practices.
Financial Analysis for Renewable Energy Project Site: Vega Economics was retained by a direct air capture company to evaluate a pro-forma financial model for the bid to develop a renewable energy project site.
Data Collection and Management: Vega Economics was retained by Fredrick County, Maryland to provide data collection, data management, and data augmentation services. Vega’s work product will assist the county in a disparity study.
COVID-19 Impact on Amazon Marketplace: Vega was retained by an online retailer to analyze how COVID-19 had impacted its sales performance. As part of the engagement, Vega processed and analyzed historical data of sales, prices, as well as rankings.
Analysis of Sample Size Sufficiency for Auditing Claims in Class Action Settlements: Vega analyzed the statistically sufficient sample size to draw scientifically valid conclusions about the veracity of claims submitted in conjunction with a class action.
Production Data Management: Vega was the designated consulting firm to receive and verify data produced by a large group of insurance companies. Our team assisted counsel by bridging the gap between the data produced in discovery and the expert analysis, ensuring the data was sufficient for the expert analysis anticipated in subsequent phases of the litigation.
Statistical Sampling in Technology Insurance Matter: The Vega team supported an expert in creating a statistically valid sample of claims to be audited in an arbitration regarding claim payments on extended service plans for mobile phones and other devices. After the audit was completed, the results were extrapolated to the overall population of claims.
Statistical Sampling Strategies in Billing Dispute: Vega created potential sampling strategies for a case related to billing disputes, allowing extrapolation of chart review results from the sample to assess liability and damages issues.
Health Insurance Pricing Models: Vega supported an expert who performed an analysis using terabytes of sensitive medical data to investigate and recreate health insurance pricing models.
Hickman-Larson Chair in Actuarial Science and Associate Professor of Risk and Insurance at Wisconsin School of Business, University of Wisconsin-Madison
- Insurance & Risk
- Data Science & Statistics
Robert Clements Distinguished Chair in Risk Management and Insurance, Tobin College of Business at St. John's University
- Insurance & Risk
- Data Science & Statistics
Managing Director, Vega Economics
- Corporate Finance
- Labor & Employment
- Securities & Finance
- Consumer Finance
- Data Science & Statistics
Associate Professor at the University of California, Berkeley
- Data Science & Statistics
Clyde F. and Ruth E. Williams Professor in Business, Johns Hopkins
- Technology, Internet & Media
- Marketing & Surveys
- Data Science & Statistics
Dean's Chair for Transformative Initiatives and Professor and Chair of Agricultural, Environmental, and Development Economics at Ohio State University
- Energy, Environment, and Natural Resources
- Environmental, Social, and Governance (ESG)
- Valuation
- Agriculture
- Data Science & Statistics
Associate Professor in the Department of Finance of the Villanova School of Business at Villanova University
- Real Estate
- Corporate Finance
- Intellectual Property
- Securities & Finance
- Financial Institutions
- Data Science & Statistics
- Antitrust & Competition
Associate Professor of Finance at the Farmer School of Business, Miami University
- Financial Institutions
- Valuation
- Data Science & Statistics
- Securities & Finance
- Antitrust & Competition
Director of the Mathematics in Finance Master's program and a Clinical Professor of Mathematics at the Courant Institute of Mathematical Sciences, New York University; Partner at CorePoint.
- FinTech, Blockchain, and Cryptocurrency
- Securities & Finance
- Valuation
- Data Science & Statistics
- Financial Institutions
Founding Director, Perihelion Capital Advisors
- Financial Institutions
- Corporate Finance
- Data Science & Statistics
Associate Professor in the Department of Economics, University of Colorado, Boulder
- Data Science & Statistics
Managing Partner at DMA Economics LLC
- Intellectual Property
- Securities & Finance
- Valuation
- Data Science & Statistics
Assistant Professor of Political Science at University of California, Berkeley
- Data Science & Statistics
Senior lecturer in the Department of Finance and the Department of Economics at the McCombs School of Business at University of Texas at Austin
- Energy, Environment, and Natural Resources
- Valuation
- Data Science & Statistics
University Lecturer at NYU Courant; Partner at CorePoint
- FinTech, Blockchain, and Cryptocurrency
- Valuation
- Securities & Finance
- Financial Institutions
- Data Science & Statistics
Assistant Professor at Cal Poly San Luis Obispo
- Labor & Employment
- Data Science & Statistics
- Antitrust & Competition
- Agriculture
Associate Professor in the Department of Economics, University of California, Davis
- Labor & Employment
- Data Science & Statistics
Professor at the University of Connecticut and Director of the Janet & Mark L Goldenson Center for Actuarial Research at the University of Connecticut
- Insurance & Risk
- Data Science & Statistics
Principal, Vega Economics
- Labor & Employment
- Data Science & Statistics
- Securities & Finance
- Healthcare & Health Economics
- Antitrust & Competition