Design, develop, and iterate on machine learning models, including causal inference, recommendation systems, clustering, and optimization models to address high-impact business problems. Experimentation & Impact Evaluation
Experimentation & Impact Evaluation
Partner with Analytics Engineering to design and evaluate experiments (e.g., A/B testing, matched cohorts, difference-in-differences) to validate model performance and quantify real-world impact.
Optimization & Decision Modeling
Develop models that inform actionable decisions, including prioritization frameworks and expected value-based optimization to drive improvements in traffic and profitability.
Model Evaluation & Continuous Improvement
Monitor, evaluate, and refine model performance using statistical methods, backtesting, and iterative experimentation to ensure accuracy, stability, and sustained impact.
Feature Engineering from Curated Data
Transform curated datasets into high-quality model inputs through feature engineering, selection, and validation, leveraging domain knowledge and statistical techniques.
Collaboration with Engineering & MLOps
Work closely with Analytics Engineering, Data Engineering, and MLOps teams to ensure models are production-ready, scalable, and effectively integrated into downstream systems.
Requirements
3+ years of experience in machine learning, applied statistics, or a related field, with a focus on developing and evaluating models in real-world applications.
Bachelor's or Master's degree in Statistics, Economics, Operations Research, Mathematics, Computer Science, or a related quantitative field; equivalent applied experience will also be considered.
Strong foundation in statistical modeling and machine learning, with the ability to explain model selection, assumptions, and trade-offs.
Experience applying a range of modeling techniques such as regression, clustering, recommendation systems, and optimization methods.
Familiarity with experimental design and causal inference techniques (e.g., A/B testing, difference-in-differences, cohort-based analysis).
Strong programming skills in Python for analysis and model development.
Proficiency in SQL and experience working with large-scale datasets in Snowflake or similar cloud data warehouses.
Experience working in AWS environments (e.g., SageMaker, EMR) and familiarity with workflow orchestration tools such as Dagster or Airflow.
#BurgerKing
Benefits at all of our global office
Additional Information
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.
As a Machine Learning Engineer II, you will be responsible for developing and iterating machine learning models that drive measurable improvements in restaurant performance, including traffic and profitability, on at scale. This role focuses on transforming large-scale transactional and operational data into predictive and prescriptive models that power data-driven decision systems across the Burger King U.S. & Canada business.
You will build and refine a range of applied machine learning solutions, including causal inference models, optimization frameworks, recommendation systems, and behavioral segmentation models. This role emphasizes strong statistical rigor, experimentation, and continuous model improvement to ensure models deliver accurate, stable, and economically meaningful outcomes.
Working closely with Analytics Engineering, Data Engineering, and MLOps teams, you will contribute to the design and evaluation of experiments and ensure models are effectively integrated into production systems, while maintaining primary ownership of the modeling lifecycle from feature engineering on curated datasets to validation and iteration.
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 .
Rbi · Corp - Miami Corporate Office