Lead Research Scientist (P1190)

84.51° Overview:

84.51° is a retail data science, insights and media company. We help The Kroger Co., consumer packaged goods companies, agencies, publishers and affiliates create more personalized and valuable experiences for shoppers across the path to purchase.

Powered by cutting-edge science, we utilize first-party retail data from more than 62 million U.S. households sourced through the Kroger Plus loyalty card program to fuel a more customer-centric journey using 84.51° Insights, 84.51° Loyalty Marketing and our retail media advertising solution, Kroger Precision Marketing.

Join us at 84.51°!

Lead Research Science (P1190)

Summary

The Lead Research Science -- Optimization will employ skills and experience to improve, create and innovate using data and complex analytics to solve current complex business problems while anticipating and charting future research needs. The role has strong research, project collaboration, and computational components. This research scientist will create new methods of solution through a combination of foundational research and collaboration with ongoing initiatives within 84.51.

Responsibilities

• Specific role presents the opportunity to develop applications for real-time optimization & decision making; price, promotion, and assortment optimization; demand forecasting (in-store & ecommerce); supply chain network; and ecommerce inventory control.
• Develop methods for deterministic, as well as stochastic optimization / optimization under uncertainty algorithms for large scale problems.
• Develop novel optimization methods that utilizes recent development in Machine-Learning and AI areas.
• Partner with the rest of the team to create, tailor, apply, and test solutions in the fields of statistical machine learning, temporal and longitudinal analyses, classification and clustering, forecasting, and optimization.
• Drive work in targeting and allocation optimization, demand forecasting, and optimization.
• Apply and improve optimization algorithms and demand forecasting models.
• Optimize access plans and analyses of huge data sets.
• Collaborate closely on applied customer science initiatives and with other Scientific Researchers, Scientific Developers, and contributors across the business.

Qualification, Skills & Experience

• PhD in optimization, operations research, industrial engineering, computer science, computer engineering, mathematics and statistics, or related subject. Dissertation or subsequent publications in the area of optimization is required, preference specifically to large-scale, combinatorial, and/or real-time optimization, and back combining ML with optimization.
  • Alternative education with extensive experience and subsequent publications demonstrating a history in delivering R&D projects may in some exceptional cases be an acceptable substitute for the PhD.
• Formulating and solving optimization / operations research problems that includes, but not limited to continuous/discrete/mixed optimization, dynamic programming, queuing theory, and machine-learning/data-driven optimization.
• Solid background researching statistical machine learning methods, forecasting, and recommender systems will be a huge plus.
• Demonstrated strong potential for discovering appropriate methods, and applying research results, to problems that arise in retail, manufacturing, and market science.
• Computational sophistication. Experience in opensource and commercial solvers, such as CPLEX, Gurobi, IPOPT, etc. Experience with Python, including Tensorflow and Spark. For some research scientist roles, experience with such languages as C or C++ and development or prototyping experience is desirable.

• Ability to create computationally efficient solutions, applying techniques from statistics, machine learning, and optimization.
• Experience in distributed and cloud platform is preferred.
• The following qualities will an edge: Natural curiosity, welcomes and embraces change. Ability to work fast yet accurately. An openness and willingness to try new things and to fail.
• Ability to work in a highly collaborative environment and to self-direct one's own research.