Data Engineer II, Burger King, US&C

Restaurant Brands International•Miami, FL

2d•Onsite

About The Position

As a Data 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 ML Ops 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.

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.

Responsibilities

Design, develop, and iterate on machine learning models, including causal inference, recommendation systems, clustering, and optimization models to address high-impact business problems.
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.
Develop models that inform actionable decisions, including prioritization frameworks and expected value–based optimization to drive improvements in traffic and profitability.
Monitor, evaluate, and refine model performance using statistical methods, back testing, and iterative experimentation to ensure accuracy, stability, and sustained impact.
Transform curated datasets into high-quality model inputs through feature engineering, selection, and validation, leveraging domain knowledge and statistical techniques.
Work closely with Analytics Engineering, Data Engineering, and MLOps teams to ensure models are production-ready, scalable, and effectively integrated into downstream systems.