Data Science Manager

We’re not in the wing business. We’re in the flavor business. It’s been our mission to Serve the World Flavor since we first opened in 1994, and we’re just getting started. 1997 saw the opening of our first brand partner operated Wingstop location, and by 2002 we had served the world one billion wings. It’s flavor that defines us and has made Wingstop one of the fastest growing brands in the restaurant industry.

Above all else – our success is largely due to our people and our core values, or what we call The Wingstop Way of being entrepreneurial, service-minded, fun, and authentic. We believe having a strong people foundation centered on these collective values creates a crave-worthy culture and talented team, as well as ensures our brand is poised for accelerated growth. We all win together.

This role contributes to Wingstop's success by collaborating with multiple departments to support data driven business decisions through building models that optimize, predict, and generates insights.

Our goal is to translate business questions into usable decision support products, visualizations & self-service tools that democratizes data across the entire Wingstop enterprise. Wingstop is looking for full-stack data scientists with experience in machine learning and algorithm development to join our team. This role reports to our Director, Advanced Analytics and will work closely with Finance, Operations, Marketing and others to support strategic growth initiatives.

The ideal candidate will have mathematical and statistical expertise, along with natural curiosity and a creative mind. While mining, interpreting, and cleaning our data, this team member will need to understand the business questions and objectives, ask questions, connect the dots, and uncover hidden opportunities for realizing the data’s full potential. As team lead, this person will act as a ‘player/coach’ getting to build, train, and improve models as well as leading others to do the same.

• Collaboration and Communication:
  
  - Work closely with stakeholders to understand business objectives and requirements.
• Collaborate with other data scientists, analysts, and team members to drive data initiatives.
• Communicate with senior leaders, breaking down more technical requirements into easy to understand concepts.
• Model Building, Optimization and Monitoring:
  
  - Develop and maintain the different environments where ML models are developed, tested and run in production
• Bring ML models into production together with a multi-disciplinary team of scientists, engineers, product managers and subject domain experts.
• Develop tools to monitor data integrity, KPIs and successful model execution for applications in production.
• Design and deploy contingency plans to protect against ML model failures.
• Contribute to improve scalability and reliability or solutions deployed in production.

• Proficient in Python and SQL, with experience applying machine learning and statistical modeling to solve complex business problems.
• Hands-on experience developing, deploying, and scaling ML and time series forecasting/prediction models in production.
• Strong programming skills in Python, with the ability to automate ML pipelines and data workflows.
• Hands on experience working in cloud data-lakes such as Snowflake and/or Databricks for scalable analytics and model building.
• Experience with data modeling (conceptual, logical, physical) to enable robust analytical and predictive solutions.
• Experience presenting to senior level stakeholders, breaking down complex problems into easy-to-understand concepts
• Preferred:
  Experience building and tuning NLP/sentiment analysis models or Generative AI applications.
• Preferred:
  Background with customer centric data including customer purchasing patterns, demographics, customer lifetime value, etc.
• Preferred: experience with data visualization tools such as Power BI or Tableau.
• Preferred:
  Familiarity with Agile (Scrum, SAFe, Kanban), MLOps, and Master Data Management (MDM) principles.
• Preferred:
  Experience in the restaurant, retail, or hospitality industry applying data science to improve operations and customer…