Analytics & Data Science

Working alongside our consultants and practice leaders, our analytics experts help clients across industries solve their biggest challenges using our expertise in data science, customer and market insights, statistics, AI, supply chain analysis, and data engineering. The Analytics Team also serves as strategic advisors to the consulting staff, providing training to advance P&C’s analytic and insights capabilities.

Working with our clients and consultants, the analytics experts hold advanced degrees in statistics, mathematics, computer sciences, and other quantitative disciplines with at least 5+ years of professional experience and backgrounds in a variety of fields including data science, marketing analytics, operations research, and BI.

  • We have expertise in advanced analytical tools and techniques that help gather and prepare multiple and complex data sets for analysis.

  • We help clients use advanced analytics and customer insights to improve customer, marketing, and product portfolio strategy.

  • We use a robust analytics and technology stack to provide cutting-edge insights for our clients.

  • Depending on the engagement, our analytics experts can act as advisors, executors of key tasks, or full team members supporting a multitude of analyses.

Analytics and Data Science Roles at P&C

Use best practice marketing and cultural research methods to collect quality data and generate transformative insights around customer, market, supplier, and employee behavior.
Apply management and decision sciences, industrial engineering, and supply chain methods to business operations through the use of advanced statistical modeling, optimization and simulation, and supply chain tools.
Support cases that require exploratory and one-time analysis using data science techniques; complex dashboards and visualizations; and strategic, insightful advice to our clients.
Apply social science principles and practices to customer and marketing questions through the use of advanced statistical modeling tools and, where relevant, machine learning.
Support cases where analytics are run repeatedly, and where software is delivered in the form of a production or prototype application; tech complexity of interfacing with client systems to extract data or carry out distributed training, requires a higher level of SW engineering capability or relevant strategy advisory cases.
Predictive modeling and advanced customer segmentation. Measurement strategy and development. Advanced customer personalization using data. Qualitative brand, customer, and experience research. Qualitative design, execution, and analysis.

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