Data Scientist - Machine Learning

WHO WE ARE

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.

WHAT WE'LL NEED

This role contributes to Wingstop's success by bridging data science and production engineering — deploying, optimizing, and scaling machine learning systems that power data-driven decisions across the enterprise. The Data Scientist-Machine Learning is responsible for taking models from development to production, ensuring they are robust, efficient, and maintainable. While data science work remains part of the role, the primary focus is on deployment pipelines, infrastructure management, and code optimization that enable the broader analytics organization to deliver impact at scale.

Key Responsibilities:

Collaboration and Cross-Functional Engagement:

Work closely with stakeholders to understand business objectives and requirements. Partner with data scientists, analytics engineers, and platform teams to move models from experimentation into production-ready systems.

MLOps and Deployment Infrastructure:

Design, build, and maintain end-to-end ML pipelines for training, validation, deployment, and retraining. Manage and optimize cloud infrastructure (e.g., Snowflake, AWS, Azure) to support scalable ML workloads and reduce operational cost.

Code Quality and Optimization:

Write clean, efficient, reusable code for ML pipelines and data processes; enforce engineering best practices through code reviews and documentation. Profile and optimize inference latency, memory usage, and throughput for models serving real-time or batch workloads. Automate testing, CI/CD workflows, and deployment processes to ensure reliable, repeatable model releases.

Model Deployment, Monitoring, and Maintenance:

Own the deployment of ML models into production environments, including containerization, versioning, and API development. Integrate ML models into existing business systems and data workflows, working closely with software and data engineering teams. Continuously tune and improve deployed models, tracking performance drift and triggering retraining as needed to maintain accuracy and reliability. Monitor and alert relevant teams to key metrics and KPI's.

Data Science Support and Insights:

Support data science efforts occasionally including exploratory analysis, feature engineering, and model prototyping where needed. Develop dashboards and monitoring tools in Power BI or Streamlit Apps or similar platforms to surface model health, data pipeline status, and key operational KPIs.

WHAT YOU'LL NEED

Bachelor's or Master's degree in a quantitative field (e.g., Statistics, Mathematics, Computer Science, Engineering, Economics, or related discipline). Cloud or ML engineering certification (e.g., SnowPro Advanced, AWS Certified ML Specialty, Google Professional ML Engineer) preferred. At least 3 years of industry experience in ML engineering, software engineering, or a related role, with proven production ML deployment experience.

Strong proficiency in Python; experience with software engineering practices including version control, testing, and code reviews. Experience with cloud data platforms such as Snowflake (including Snowpark) and cloud infrastructure (AWS, Azure, or GCP). Hands-on experience with Python, Java, or Scala; demonstrated ability to write production-grade, reusable code to automate ML pipelines and data workflows. Experience with containerization and orchestration tools (e.g., Docker, Kubernetes, Airflow, or similar) for managing ML workflows. Experience with data and analytics tools (e.g., Snowflake, Profisee, SAP BW, Power BI, Tableau)

Preferred Qualifications:

Experience with ML deployment on Snowflake Experience building and tuning time series forecasting or demand prediction models Experience with CI/CD pipelines for ML systems (e.g., GitHub Actions, Jenkins, MLflow, or similar MLOps tools) Experience in restaurant, retail, or hospitality Experience with SDLC methodologies, including Agile frameworks (Scrum, SAFe, Kanban) Understanding of Master Data Management principles, architectures, and patterns

WHO YOU ARE

No job is too small. You recognize that the real work happens in the restaurants, and everything we do should support their success. You stay humble, roll up your sleeves, and always look for ways to help. You learn from others and contribute wherever you can. You care deeply about doing great work and driving results. You're curious, ask questions, and seek out opportunities to improve. You don't just point out problems—you bring solutions, ideas, and perspectives that move the team forward. You take full responsibility for your work and see things through to completion. You aren't afraid to fail because you know that failure is a part of learning and growth. You take action, move fast, and keep pushing forward. You lead with empathy, respect, and emotional intelligence. You collaborate effectively, fostering a culture of trust and constructive feedback. You understand the importance of teamwork and ensure that your actions build others up rather than break them down. You push yourself and others to be better. You embrace healthy conflict, knowing that great ideas and strong teams emerge from honest, constructive conversations. You believe that leaders create leaders and are committed to fostering a culture of growth, challenge, and continuous improvement.

BENEFITS

Flavor Perks:

Unlimited paid time off for exempt employees One paid volunteer day of your choice Competitive bonus structure for eligible roles Team member stock purchase plan Health savings or flexible spending account options 401k - (dollar for dollar on the first 3% and then 50 cents on the dollar for the next 2% for team member contributions up to 5% of eligible compensation) Comprehensive medical, dental, and vision benefits Basic life and AD&D insurance provided Pet insurance Education Assistance Wellness reimbursement program Paid maternity and paternity leave Fun is the best

Flavor:

Lunch provided every Tuesday and Thursday in office Discount on Wingstop gift cards Onsite game room and patio Wingstop provides equal opportunities for everyone that works for us and everyone that applies to join our team, without regard to sex or gender, gender identity, gender expression, age, race, religious creed, color, national origin, ancestry, pregnancy, physical or mental disability, medical condition, genetic information, marital status, sexual orientation, any service, past, present, or future, in the uniformed services of the United States (military or veteran status), or any other consideration protected by federal, state, or local law.