Note: The job is a remote job and is open to candidates in USA. Restaurant Brands International Inc. is one of the world's largest quick service restaurant companies, and they are seeking a Machine Learning Engineer II to develop and iterate machine learning models that enhance restaurant performance. The role involves transforming large-scale data into predictive models and collaborating with various engineering teams to integrate these models into production systems.
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, backtesting, 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
Skills
- 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
Benefits
- Comprehensive global paid parental leave program that supports employees as they expand their families
- Free telemedicine and mental wellness support
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