Director, Research Science (P1072)

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°!

Director, Research Science (P1072)

Summary

The Director, AI / ML Researcher will employ deep experience to explore, discover, advance the state-of-the-art-science, and innovate solutions to critical business problems at scale. The senior AI/ML Researcher will create, customize, and apply leading-edge science and software in areas including machine learning, optimization, AI, and forecasting. The methods will be customized to rich and diverse data in our business domains of interest to shape and progress our current and future research directions by scaling, deploying, and integrating them into the business to drive value. The role has both strong research, software engineering / architecture, and computational components.

Responsibilities

• Research and create technical solutions in areas of importance to 84.51°, including, but not limited to: AI/ ML, statistical analyses, classification and clustering, and optimization.
• Two qualities are must for this role: (1) fearless in learning new science and technology and (2) Willing to operate and make considerable progress under unclear business requirements.
• Help shape research direction by investigating and assessing new methods from the fields of machine learning, AI, optimization, and the data sciences.
• Select method of solution, lead, and coordinate research initiatives, as well as collaborate with other Research Scientist, Scientific Developers and thought leaders across the company.
• Partner with dev teams to develop new tools, processes, and operational capabilities to monitor and analyze model performance and data accuracy where needed
• Help shape technical architecture of solutions by investigating and assessing new methods from the fields of machine learning, AI, optimization, and the data sciences.
• Understand business requirements and trade-off scale, risk, and accuracy to maximize value and translate research into research prototypes that can be tested and deployed.
• This will be a huge plus: Build production-grade solutions (robust, reliable, maintainable, observable, scalable, performant etc.) to manage and serve machine learning models and science solutions
• Lead, develop and mentor a team of individuals.

Qualification, Skills & Experience

• PhD or Masters in machine learning / AI, computer science, computer engineering, mathematics and statistics, operations research, or related subjects. Outstanding and contributions in the retail or customer domain space may in some exceptional cases be an acceptable substitute for the PhD degree when the focus of the specific role is more applied rather than foundational research.
• Demonstrated ability to self-direct one's own research and set the research agenda for more junior research scientists and dev teams.
• Demonstrated ability to self-discover the best methods to implement science at scale and lead more junior researchers and developers to do so.
• Comfort with independent learning of new sciences and technologies, and willingness to jump into unfamiliar areas.
• Research experience and track record of high-quality peer-reviewed scientific publications in the technical domain, and/or other peer-reviewed recognitions of technical contributions.
• Computational sophistication. Experience with Python, Spark, APIs, Cloud, Distributed / Parallel Compute.
• Following skills will be a huge plus:

• Ability to create computationally efficient solutions, applying profiling and benchmarking techniques will be a huge plus

• Hands-on experience in the full end to end developing software solutions that scale and leveraging CI/CD and MLOps to develop, test, and deploy.
• Experience building large-scale algorithmic solutions that have been successfully delivered to stakeholders.
• Knowledge of End-End Machine Learning pipeline and MLOps tools (e.g., Model registry, Experiment tracking, Feature Store, Model monitoring)
• Strong ability to communicate results, and expertise with visualization of large complex data sets would be a plus.
• High level of independence; ability to make time-sensitive decisions rapidly and solve problems with the information / data at hand.
• Strong time and project management skills; the ability to balance multiple, simultaneous work items and prioritize, as necessary.
• Proven record of leading technical teams and turning loose ideas into business solutions to drive value