Egor Kudriavtcev at Simon Business School

Egor Kudriavtcev

Personal website

Hello everyone!

I am a Ph.D. Candidate in Marketing at Simon Business School.

I am a Data Scientist with deep knowledge of Economics, Marketing, and Econometrics.

My research interests include:

  • Machine Learning applications in Marketing
  • Aggregate demand models with random coefficients
  • Structural interpretation of Machine Learning algorithms
  • Individual demand estimation with Neural Networks
  • Estimation of product maps and product substitution
  • Studying online word of mouth

I have extensive experience in working with wast variety of data across different industries. My large toolset let me getting valuable insights from the data with ease, while my strong business intuition and judgment let me prioritize the findings in the data according to their business value.

My business interests include:

  • Experiment design (such as A/B testing)
  • Retention prevention
  • Conversion rate improvement
  • Estimation of customer lifetime value
  • Demand forecasting
  • Optimal pricing
  • Personalization of promotions
  • Improvement of customer engagement
  • Text-mining of customer reviews
  • Construction of product perceptual maps
  • Churn rate prediction

My quantitative modeling interests include:

  • Intuition and interpretation
  • Performance improvement
  • Causal Inference
  • Metamodeling
  • Sensitivity Analysis
  • Statistical confidence of the results
  • Numerical complexity and scalability (big data)
  • Numerical stability
  • Reliability and generalizability
  • Parallelization and optimization
  • Visualization

Greg Shaffer's Website-

Selected Projects


Automatic pricing competition. An experiment with simulated Amazon seller.

Joint work with Jeanine Miklos-Thal and Catherine Tucker
Made in Python with Django

It is a study on how the presence of automation tools on such platforms as Amazon influences the long-term pricing equilibria. At the core of this research project, we are looking at how individuals make their decisions in an online pricing simulation, which imitates Amazon sellers. Each seller is operating in two online marketplaces simultaneously. In every marketplace, the seller has to choose his own price. In one of the marketplaces, sellers are allowed to use automated pricing (provided by the platform) to help them set their prices, but not in another. Each seller is facing two competitors in each marketplace.

Economics keywords: market equilibrium, pricing automation, experimental economics
Computer Science keywords: Cloud Services, AWS EC2, Django, linux KVM


Product Launches with New Attributes: A Hybrid Conjoint-Consumer Panel Technique for Estimating Demand. Neural network estimation.

joint work with Mitchell Lovett and Bhoomija Ranjan
Made in Python, R, Keras

The conjoint study is a great tool for making predictions of individual preferences for new-to-market product attributes. Unfortunately, this tool measures only stated preferences, which prompt of different types of biases. The demand estimation of revealed preferences provides much more accurate results for existing products but can’t be used for non-existing product attributes. This paper provides a hybrid method of demand estimation, which combines the information from both conjoint surveys and consumer panel purchase data. The core idea of the method lies in utilizing neural networks which are built in a structural way (NN+S approach), and further regularized and tuned for best out-of-sample prediction.


Improving the substitution patterns of aggregate demand models

Made in Julia, Python, R

Modern aggregate demand models with random coefficients rely on observed product characteristics to account for substitution patterns between products. This project investigates an application of matrix factorization approach for estimating unobserved product characteristics from online ratings in the beer industry. Estimated unobserved characteristics have several advantages over observed characteristics, including relevance, interpretability and low dimensionality. This technique can also be applied for estimating unobserved product characteristics to improve the estimates of the substitution between products in such industries as video games, movies, books.

  • The main internal loop is implemented with Julia, which makes the algorithm lighting fast (zero memory allocation)
  • Overcame the problem of numerical stability which is commonly reported by researchers
  • Custom made gradient-based optimization algorithm

Data Science keywords: data cleaning, PCA, SVD, hyper-parameters tuning, regularization, cross-validation, cluster computing, shell scripting
Economics Keywords: cross-price elasticity, GMM, bootstrapping, BLP


Matrix factorization using neural networks

Made in Python

Matrix factorization algorithms (such as Singular Value Decomposition) are very popular in recommender systems. The goal of this project is to estimate SVD using a neural network, which improves the convergency to the global minimum. (Almost any mathematical model can be expressed as a custom neural network and can be trained using standard Neural Network tools, such as Keras, Tensorflow, and PyTorch)

Data Science keywords: web scraping, neural networks, SVD, hyper-parameters tuning, regularization, cross-validation
Economics Keywords: product map, individual tastes heterogeneity, individual scale usage


Human-like web-scraping

Made in R

In some situations, you can not scrape online data as a robot. To get the information your program needs to behave as a human. Take some time to think, react to on-screen events, move the mouse cursor as a human. It turns out that RSelenium package and a lot of work can make it possible.

  • Fault-tolerant web scraping performed by multiple robots in parallel
  • A unique identity of each robot
  • An extra process which orchestrates all the robots to make a stratified sample

Getting insights from text-mining of online reviews

Made in R

Text-mining can be used for investigating product closeness. An underlying assumption is that products with similar user experience will receive similar user reviews.

Data Science keywords: web scraping, text-mining, stemming, stop words removal, synonyms pooling, multidimensional scaling


Analysis of keywords search money spending

Made in R

There are plenty of online tools for analyzing keywords. None of them is better than the data from your own advertising campaigns. The idea was to combine the data from individual keywords advertising campaigns and spacial locations of the competitors to increase the conversion rate per dollar spend. This project is still in development.


Quasi-experiment in online reviews

Made in R

Econometric study on the presence of social bias in online user reviews in the context of the video game industry. The study exploits the fact, that some websites report average ratings for the games by aggregating for each platform separately (PC, Xbox, etc.), while other websites report average ratings for the games by pooling all the platforms together.

Economics keywords: diff-in-diff, quasi-natural experiment


Continuous web-scraping

Made in C#, MySQL

In some cases, you need to visit a page multiple times to get a single observation. The goal of the project is to develop a library for gathering such observations. The library was used in several further projects.

  • Fault-tolerant custom made ORM
  • 100'000 requests per day from a single computer

Software for semi-automatic parsing huge text of data

Made in Java

The idea was to quantify the information from a particular book. The book was scanned and recognized with a lot of mistakes. The number of mistakes didn't allow to develop a fully automatic parser. The solution was to develop a semi-automatic parser, which was doing all the heavy lifting automatically while asking an operator (human) to resolve the most fault critical situations.


LAN based games with stabilo-cheers synchronization

Made in C++ using RakNet, Irrlicht, .NET

The goal of the project was to develop software, which allows conducting different experiments in the Laboratory of Experimental Economics. The software was able to track the movement of the center-mass of individuals and mouse movements while individuals were playing economic games by LAN.

Research Institutions

2012  -  2014

Gaidar Institute of Economic Policy

Moscow, Russia
  • Implemented modern international-trade models
  • Case-studies of Non-tariff measures
  • Prepared international-trade reports
2009  -  2011

Laboratory of Experimental Economics

Moscow Institute of Physics and Technology, Moscow, Russia


  • Developed LAN-based games for Experimental Economics
  • Initiative: introduced more advanced measuring system, which combines stabilo-cheers measurements and mouse movements
2007  -  2009

Institute of Economic Forecasting

Moscow, Russia


  • Leontief input-output models

Private Companies

2014  -  2015

OAS Enterprises

Moscow, Russia

Fixed income Analyst

  • Fundamental analysis of high-yield corporate bonds
  • Maintained the information system of the fund
  • Initiative: Introduced auto-generated reports using Bloomberg Terminal and VBA for Excel
2014,  2015

Strategy Partners Group

Strategic Consulting, Moscow, Russia

Freelancer (Econometrician)

  • Applied econometric analysis (using Stata)
  • Several completed projects

Startup "Evanti"

Moscow, Russia

Expert in Experimental Economics

  • Successfully completed investment memorandum



The Simon Business School

Rochester, NY, USA

Lab instructor

  • Marketing Research
  • Marketing Analytics using R

Higher School of Economics

Moscow, Russia

Lab instructor

  • Probability Theory and Statistics

International College of Economics and Finance

Moscow, Russia

Lecturer, Lab instructor

  • Introduction to programming for students
  • Advanced techniques in Excel
  • VBA scripts for Excel

New Economic School

MS in Finance program, Moscow, Russia

Lab instructor

  • Financial Econometrics I
  • Financial Econometrics II

State Academic University for Humanities

Moscow, Russia

Lecturer, Lab instructor

  • Econometrics
  • Time-Series Econometrics

Evening School of Engineering and Physics

Moscow Institute of Physics and Technology, Moscow, Russia

Lecturer, Lab instructor

  • Advanced Mathematics for pupils

Master Degrees

2017  -  present

The Simon Business School

Rochester, NY, USA
Master of Science
Business Administration
  • Ph.D. Candidate
  • Five years of Pd.D. research
  • Intense training in Quantitative Marketing
2011  -  2013

New Economic School

Moscow, Russia
Master of Arts
  • Top 1 hardest Universities in Economics in Russia
  • Intense training in Economics and Finance
2008  -  2010

Moscow Institute of Physics and Technology

Moscow, Russia
Master of Science
Applied Math. and Physics
Applied Economics
  • Top 1 hardest Universities in Physics in Russia
  • Intense training in Applied Mathematics and Experimental Economics

Bachelor Degree

2004  -  2008

Moscow Institute of Physics and Technology

Moscow, Russia
Bachelor of Science
Applied Math. and Physics
Applied Mathematics
  • Top 1 hardest Universities in Physics in Russia
  • Intense training in Applied Mathematics


My favorite way of working with data is through atom connected remotely to a server with hydrogen. This way I can use R, Python, and Julia in one powerful text editor and enjoy the benefits of all three worlds. RStudio and Jupyter are also commonly used tools, which simplify the results sharing with Jupyter Notebooks and R Markdown.

Programming Languages

  • R
  • Python
  • Julia
  • C#
  • Java
  • C++
  • Stata
  • MS VBA
  • JavaScript
  • Delphi

Quantitative Methods

  • Experimental Design
  • Linear Regression
  • Logistic Regression
  • Clustering
  • Multidimensional Scaling
  • Instrumental Variables
  • General Method of Moments
  • Regression Discontinuity
  • Quasi-Random Experiments
  • Matching
  • Random Forest
  • Monte-Carlo Simulations
  • Bayesian Approach
  • Principal Component Analysis
  • Neural Networks
  • Support Vector Machine
  • Nearest Neighbors
  • k-means
  • Attribution Model
  • Collaborative Filtering
  • Deep Learning
  • Survival Analysis
  • Matrix Factorization
  • Singular Value Decomposition
  • Ensembling
  • Gradient Boosting

Favorite Libraries

  • keras
  • tidyverse
  • plotly
  • ggplot
  • stargazer
  • RSelenium
  • pandas
  • numpy
  • data.table
  • magrittr
  • linq
  • scipy
  • sklearn
  • feather
  • statsmodels
  • pyblp
  • foreach (R)
  • matplotlib
  • xgboost


  • Excel
  • Tableau
  • Linux KVM
  • AWS
  • json
  • XML
  • SQL
  • LaTeX
  • Bloomberg terminal
  • Dot NET
  • HTML
  • WordPress
  • django
  • OpenGL
  • Unity3d
  • Irrlicht
  • jMonkey

Free Software

Hand movement rehabilitation

Made in Unity3d (C# coding)

I created a video game for hand movement rehabilitation. The purpose of the game is to touch different colorful butterflies. There is no end of the game, no pressure, just changing relaxing visual sceneries and randomly generated butterflies. The game is compatible with different types of input devices, including regular mouse, 3d mouse, joystick, gamepad, touchpad, trackball. The game was used for after-stroke rehabilitation of hand movement.

Please, feel free to use it for personal purposes.

Windows version (Dropbox download)

Screenshot from the game

Created by Egor Kudriavtcev with django
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