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Hire Dr. Andrew V.

United States

USD 200 /hr

Ph.D. Operations Research Expert | Optimization, Predictive AI, Supply Chain & Revenue Management | 20+ Yrs Exp

Profile Summary

Dr. Andrew Vakhutinsky is a Principal Operations Research Scientist and Applied AI Leader with over 20 years of experience designing and deploying large-scale mathematical optimization, statistical, and machine learning models for retail, supply chain logistics, revenue management, and telecommunications. He holds a Ph.D. in Operations Research from the University of Maryland and an M.S. in Applied Mathematics and Physics from the Moscow Institute of Physics and Technology. Throughout his career at organizations such as Wayfair, Oracle Labs, Staples, Revionics, and Akamai, Dr. Vakhutinsky has spearheaded R&D projects that translate complex stochastic data into explainable, high-impact decision systems. Core Capabilities & Technical Expertise - Mathematical Programming & Optimization: Mixed-Integer Linear Programming (MILP), Stochastic Programming, Heuristics, Network Routing & Scheduling, and Inventory Allocation. - Advanced Solvers & Tools: GUROBI, CPLEX, FICO Xpress, Pyomo, OR-Tools, SCIP, and HiGHS. - AI, Machine Learning & Forecasting: Predictive modeling (PyTorch, TensorFlow, GCP Vertex AI), Quantile Regression, Causality/Econometric Analysis, and Discrete Choice Modeling. - Revenue Management & Retail Analytics: Dynamic pricing, markdown optimization, overbooking models, demand driver estimation, and space allocation. - Modern Generative AI Integration: Combining LLMs, AI coding agents (Cursor), and reasoning frameworks (Tree/Chain-of-Thought) with classical optimization models for explainable AI policies (e.g., OptiChat). Research, Patents & Publications - Patents: Inventor or co-inventor on 9+ granted and pending U.S. patents covering federated learning, overbooking algorithms, room assignment optimization, and differential pricing. - Publications: Published in top peer-reviewed journals and conferences including Manufacturing & Service Operations Management (MSOM), INFORMS Journal on Computing, IEEE, and Neural Networks.

Subject Matter Expertise

• ★ Algorithm Design
• ★ Predictive Modeling & Analytics
• ★ Decision Science
• ☆ Data Analysis
• ☆ Data Exploration
• ☆ Cluster Analysis
• ☆ Big Data Analytics
• ☆ Machine Learning
• ☆ Pattern Recognition
• ☆ Deep Learning
• ☆ Business Analytics
• ☆ Python
• ☆ SQL
• ☆ Java
• ☆ Tensorflow
• ☆ LaTex
• ☆ Scikit-learn

Services

• Research
• Consulting
• Data & AI
• Product Development

Research Scientific and Technical Research

Consulting Operations Consulting, Scientific and Technical Consulting, Manufacturing Consulting

Data & AI Predictive Modeling, Statistical Analysis, Algorithm Design-Non ML, Algorithm Design-ML, Big Data Analytics

Product Development Formulation

Work Experience

Senior Scientist

Wayfair

November 2025 - Present

Consulting Member of Technical Staff

Oracle

March 2018 - September 2025

Sr. Scientist

Staples

January 2016 - February 2017

Prinicipal Scientist

Revionics

January 2013 - December 2016

Prinicipal Scientist

Oracle

January 2010 - December 2012

Senior Software Engineer

Akamai Technologies

January 2007 - December 2009

Lead Research Analyst

BAE Systems

January 2003 - December 2006

Education

Operations Research

University of Maryland, College Park

September 1991 - October 1996

Physics and Applied Mathematics

Moscow Institute of Physics and Technology

September 1982 - May 1988

Certifications

• Private Pilot. Instrument rating for single engine airplane

  Federal Aviation Administration

  June 2005 - Present

Publications

JOURNAL ARTICLE

Andrew Vakhutinsky, Övünç Yılmaz, Farbod Ekbatani, Zifeng Zhao, Ruxian Wang (2026). Data-Driven Pricing for Availability-Based Upgrades Under a Multiple Binary Choice Model with Copula . Manufacturing & Service Operations Management.