Job description

About the Role Build, validate, deploy, and maintain Data Science and Optimization models pertaining to Pricing Optimization, Inventory Optimization, and Fulfillment Optimization. Design and implementation of sophisticated experimental frameworks utilizing advanced methodologies such as synthetic pairs, synthetic controls, and randomized controlled experiments to rigorously measure causal impacts on key performance indicators, leveraging robust statistical techniques, including difference-in-differences and regression analysis, while conducting comprehensive sensitivity analyses and producing actionable insights. Develop End to End simulation pipelines to accurately and realistically mimic business phenomenon and deploy the simulation to measure model performance. Develop data validation and visualization dashboards to lead model validation and adoptions collaborations with functional stakeholders. Collaborate with Inventory Management, PdM, and Strategy teams, as well as the central Data Science & AI team, to set up data science roadmaps and measure actions resulting from analytics recommendations. Telecommuting permissible from any location within US. Salary Range: $158,642 – $177,000 Employee pay will vary based on factors such as qualifications, experience, skill level, competencies and work location. We will meet minimum wage or minimum of the pay range (whichever is higher) based on city, county and state requirements. What You'll Do 1. Linear models, Tree Based models; 2. Deep learning models to leverage large data sets and to define various allocation optimization, price optimization or fulfillment optimization policies; 3. Time Series Modeling to analyze and forecast data collected over time to identify trends and patterns in sales fluctuations; 4. At least two of the following areas: Inventory Optimization, Promo & Markdown9 Optimization, and Fulfillment Optimization; 5. Simulation Design; 6. Experiment design and Measurement including defining hypotheses, selecting appropriate experimental variables (independent and dependent), using techniques like randomization, sampling, and control groups to reduce bias and ensure validity, hypothesis testing, A/B testing, and regression analysis to measure causal relationships and derive actionable insights; 7. Visualization tools like Tableau, Power BI; 8. Bayesian Machine Learning Models; 9. Data Mining to discover insights from large datasets; 10. Python Programming Language & Data Science Libraries like Scikit; and 11. Cloud infrastructure Azure, AWS, or DataBricks Who You Are Masters’s degree or foreign degree equivalent in Data Science, Operations Research, Statistics or related field and three (3) years of experience in Data science or in the job offered.

Requirements