Data Science, AI & Statistical Modeling

One of the most pressing challenges companies face today is how to harness the ever-growing expanse of data to help solve real-world problems. Analysis Group clients benefit from our depth of quantitative expertise in handling big data, our understanding of the technological landscape of available tools and methods, and our grasp of how to deploy those resources to extract knowledge and generate insights that can inform critical decision-making processes. Regardless of the approach taken or the complexity of the material, we present our analyses in compelling ways that are actionable and easy to understand.



Our data science experts draw on tools from AI, machine learning, natural language processing (NLP), causal inference, statistical simulation, and data visualization to craft practical solutions. By integrating emerging technologies such as generative AI (GenAI), we continue to push the boundaries of data science applications, delivering innovative solutions that drive efficiency, clarity, and strategic value for our clients.

We go beyond traditional analytics and focus on extracting knowledge and insights from the data. This allows us to identify patterns and generate more accurate and powerful predictive models that, combined with our deep experience in economic analysis, help inform client decisions. These techniques can be applied in litigation involving antitrust and competition, intellectual property, and securities and finance, among other areas. They can also support clients in non-litigation contexts such as health care analyses and strategy and policy analytics, as well as with market research and survey design and implementation.

Our experience applying data science and AI to economics, finance, health care analytics, and business strategy are numerous and wide ranging, including:

  • Developing GenAI tools that use large language models (LLMs) to extract and summarize key information from large text corpora, audio, and video files, enabling more efficient analysis of complex datasets
  • Designing a GenAI disease progression modeling methodology capable of simulating synthetic patient profiles and disease trajectories
  • Analyzing online product reviews to help determine whether and to what extent allegedly infringed features made a difference in consumers’ purchasing decisions
  • Using NLP to detect fraud, including by analyzing the keyword frequency and phrase structure of insurance claims
  • Using machine learning to detect securities market manipulation
  • Predicting the prevalence of an undiagnosed or under-diagnosed disease that otherwise would have been elusive
  • Predicting disease progression
  • Predicting treatment resistance using large genomic databases
  • Using big data algorithms to identify adverse events in social media
  • Using predictive analytics to help forecast the market demand for new products or features and/or the success of marketing campaigns