James G.


James Galanis is a data and marketing scientist working in the eCommerce, energy, technology, transportation and financial sectors.

In my work, across all methodologies, I am extremely focused on the translation of modeling diagnostics and output to clear visualization and insights.

Broad analytical expertise includes advanced multivariate statistics, machine learning, survey research and experimental design, operations research and financial modeling.

Focused expertise includes online research (design, crosstabs, log-linear, logistic and post-stratification weighting), econometrics (single equation, simultaneous equation, SEM and panel), forecasting (econometric, bass-diffusion and simulation) and marketing sciences (mix modeling areas: marketing, media and multi-attribute as well as predictive modeling, segmentation, market sizing and price modeling).

Professional experience spans multiple industries across several sectors – international, Federal, state, non-profit, private, venture capital and academic. Employers include Intel, eBay, Universal McCann, Charles Schwab, AKQA and Uber.

Consulting clients include Facebook, Uber, Audi, American Express, Clorox, FedEx, Cognisights, US EPA, Aquatic Environments and Opinion Technology.

Dr. Galanis holds a BA from Columbia University and an MS and PhD in Energy Management & Policy from the University of Pennsylvania.


Survey and Experimental Research: research design, sampling, wording and logic, fielding, tabs and weighting;

Advanced Multivariate Statistics: contingency, loglinear, bootstrapping, conjoint, mixed models and ANOVA;

Econometrics (Time Series): single-equation, simultaneous-equation, panel, ARIMA and VAR variants;

Econometrics (Causal Impact): Experiments (controlled, natural), IV, discontinuity, diff-in-diff, PSA and SEM;

Machine Learning: GLM, logistic (binary, multinomial), trees, model selection and dimensional reduction;

Financial Modeling: NPV, IRR, cost-benefit, life-cycle, Monte Carlo simulation and mathematical programming.