Location: Cincinnati, OH / New York City, NY / Chicago, IL
Position: Statistical Model Research Scientist
or email to Laurence.Wilson@8451.com
Who We Are
In a digital economy data is the 21st century gold rush. Imagine working for a Big Data company with 10 Petabytes of data at your fingertips. A company where Analysts and Scientists derive insights from 35+ Terabytes of new data every week.
84.51° is a wholly owned subsidiary of The Kroger Company. Our data is sourced from consumer transactions and behavior across 24 banners, 38 states and 62 Million households. We are the research, development and innovation arm of the Nation’s second largest retailer.
We are a community of analysts – we are researchers in search of the customer story. We are the voice of the customer. Customers share their story with us each and every day, and it is our job to tell their story. Our Analysis team is known for solving client and customer problems, employing the most appropriate statistical and mathematical analytic approaches.
We expect all analysts to be hands on with respects to the analysis of a retailer database, using the full suite of 84.51° technical tools – SAS, SQL, R, Excel, and other specific internal tools – to create timely, relevant, and actionable insights.
The Research Scientist will employ skills and experience to improve, create and innovate data-driven modeling approaches for our price and promotion solutions, while anticipating and charting future research needs. The role has both strong research, modeling, and computational components. Additionally, the role will require exploring and examining data from multiple sources to uncover insight that will help price and promotion science.
- The Research Scientist will create solutions in areas of importance to 8451, including, but not limited to:
- Applying machine learning techniques (regression, classification, prediction, and clustering) to extremely high dimensional data
- Developing extremely high dimensional cross category sales demand models
- Time series analysis and online learning
- Model validation and calibration
- Analyzing data from many sources, and applying statistical methods to gain insights
- Driving applications of research to problems that arise in retail, manufacturing, and market science
- Developing resource allocation/optimization algorithms and recommender engines that utilize cutting edge forecasting models
- Project responsibilities run from requirement formulation, literature exploration, selection of methods, creation and prototype of solutions, and working closely with other Scientific Researchers, Scientific Developers, and Software Engineers to productionize the solution.
Qualifications and Preferred Skills
Typical Research Skills and Experience Include
- Strong statistical / machine learning background
- Solid background researching statistical machine learning methods, especially forecasting, supervised learning, classification and classification trees, Bayesian methods.
- Experience with high performance computing
- Knowledge of techniques for robust modeling of high dimensional data is essential
- Developing extremely high dimensional cross category sales demand models is preferred
- Experience with time series analysis
- Model validation
- Comfortable with analysis of data on multiple scales from many sources including unstructured data
- Experience in price, promotion, and assortment modeling is preferred
- Experience with optimization is preferred: both continuous and discrete
Desired Computation Skills Include
- R and Python development or prototyping expertise
- Experience with multi-threaded and cluster computing
- Experience with SQL
- Experience in C++ is preferred
- Experience with Unix/Linux environments.
- Ability to create computationally efficient solutions, applying techniques from statistics and machine learning
- Experience with Big Data concepts, tools and architecture.
The successful candidate will have a PhD in computer science, computer engineering, mathematics, statistics, operations research, or related subject; and/or extensive work or other experience demonstrating expertise utilizing these skills. Alternative education with extensive experience demonstrating track record with R&D projects will be considered.