Principal Machine Learning Engineer job opportunity at Restaurant Brands International.

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DatePosted 14 Days Ago bot
Restaurant Brands International Principal Machine Learning Engineer
Experience: 4-years
Pattern: full-time
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loacation Corp - Miami Corporate Office, United States Of America
loacation Corp - Miami C..........United States Of America

Ready to make your next big professional move? Join us on our journey to achieve our big dream of building the most loved restaurant brands in the world.    Restaurant Brands International Inc. is one of the world's largest quick service restaurant companies with nearly $45 billion in annual system-wide sales and over 32,000 restaurants in more than 120 countries and territories. RBI owns four of the world's most prominent and iconic quick service restaurant brands – TIM HORTONS®, BURGER KING®, POPEYES®, and FIREHOUSE SUBS®. These independently operated brands have been serving their respective guests, franchisees and communities for decades. Through its Restaurant Brands for Good framework, RBI is improving sustainable outcomes related to its food, the planet, and people and communities. RBI is committed to growing the TIM HORTONS®, BURGER KING®, POPEYES® and FIREHOUSE SUBS® brands by leveraging their respective core values, employee and franchisee relationships, and long track records of community support. Each brand benefits from the global scale and shared best practices that come from ownership by Restaurant Brands International Inc. Title: Machine Learning Engineer, QSR Forecasting & Labor Optimization Location: Miami, FL (USA) Reports to: Sr. Director, Data & Software Engineering What You'll Do: Design forecasts optimized for different decision horizons (same-day labor adjustments, weekly schedules, long-range planning) Design monitoring and governance processes to ensure forecast stability, fairness, and operational trust Own model lifecycle management including monitoring, alerting, retraining cadence, and rollback strategies Build and maintain store-level and daypart-level forecasting models for sales, transactions, and labor demand Apply time-series analysis, regression, and ensemble machine learning techniques to short- and long-horizon forecasts Develop models that account for: Seasonality (daily, weekly, annual), promotions, limited-time offers, and pricing changes, holidays, local events, weather, and store-specific effects Leverage algorithms such as:   XGBoost / LightGBM / CatBoost, Random Forest and Gradient Boosting Engineer features from POS, labor, calendar, and external datasets Evaluate model performance using appropriate QSR-relevant metrics (e.g., MAPE at store/daypart level) Partner with operations, finance, and workforce management teams to translate forecasts into staffing recommendations Productionize models and support ongoing monitoring, retraining, and performance optimization Document modeling assumptions and results for both technical and operational stakeholders What You'll Need To Succeed: 4+ years of experience building forecasting or predictive models in production Bachelor’s or Master’s degree in Mathematics, Statistics, Computer Science, Engineering, Operations Research, or a related field Strong foundation in probability, statistics, linear algebra, and optimization Strong quantitative and analytical skills Hands-on experience with: XGBoost, Random Forest, Gradient Boosting models, Python (NumPy, Pandas, scikit-learn, stats models) Tools: Git, notebooks, experiment tracking, cloud ML platforms (preferred) Data: POS data, labor data, promotions, calendar and event features Experience modeling time-series and panel data across many locations Ability to clearly communicate mathematical concepts to non-technical business partners Ability to problem-solve with an operations mindset, communicate clearly and influence operational decisions Ability to work with noisy, real-world QSR data Experience in QSR, retail, hospitality, or multi-unit operations Familiarity with: ARIMA/SARIMA, State-space models, or Prophet Model interpretability tools (e.g., SHAP) Experience deploying ML models at scale (batch or real-time) Exposure to workforce management, labor scheduling, or demand planning systems RBI follows a 5 day, in-office work schedule to support collaboration. Candidates should be comfortable working onsite 5 days per week out of our office in Miami, FL. Benefits at all of our global offices are focused on physical, mental and financial wellness. We offer unique and progressive benefits, including a comprehensive global paid parental leave program that supports employees as they expand their families, free telemedicine and mental wellness support. Restaurant Brands International and all of its affiliated companies (collectively, RBI) are equal opportunity and affirmative action employers that do not discriminate on the basis of race, national origin, religion, age, color, sex, sexual orientation, gender identity, disability, or veteran status, or any other characteristic protected by local, state, provincial or federal laws, rules, or regulations. RBI's policy applies to all terms and conditions of employment. Accommodation is available for applicants with disabilities upon request.

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