84.51° is a retail data science, insights and media company. We help the Kroger company, consumer packaged goods companies, agencies, publishers and affiliated partners create more personalized and valuable experiences for shoppers across the path to purchase.
Powered by cutting edge science, we leverage 1st party retail data from nearly 1 of 2 US households and 2BN+ transactions to fuel a more customer-centric journey utilizing 84.51° Insights, 84.51° Loyalty Marketing and our retail media advertising solution, Kroger Precision Marketing.
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The Lead Research Scientist (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, prototyping, and project collaboration. This research scientist will create new optimization methods through a combination of foundational research and collaboration with ongoing initiatives within 84.51. Also, the role has computational elements, where candidate will have the opportunity to develop, design, prototype, and lead the implementations of new optimization algorithms for large data sets.
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. Ensure application viability for real time data driven decision making. Develop methods for deterministic, as well as stochastic optimization / optimization under uncertainty algorithms for large scale problems. Develop novel optimization methods that utilize recent developments in Machine-Learning areas. Develop novel numerical techniques to improve computational efficiency.
QUALIFICATIONS, SKILLS, AND EXPERIENCE:
- PhD in optimization, operations research, industrial engineering, computer science, computer engineering, mathematics and statistics, or related subjects. Dissertation or subsequent publications in the area of optimization is required, preference specifically to large-scale, combinatorial, and/or real-time optimization.
- Alternative education with extensive experience and subsequent publications demonstrating track record 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, queing theory, and machine-learning/data-driven optimization, etc.
- Experience in performing data analysis, cleaning, and orchestration to enable optimization sciences.
- Analyzing large-scale network optimization problems.
- Experience with real-time optimization/decision making models.
- Using computational efficiency analysis in designing optimization methods.
- Utilizing statistical learning methods including forecasting, supervised learning, classification trees, and neural networks.
- 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.