Sr Machine Learning Engineer - Labor Management Forecasting (Hybrid)
About The Position
Join Publix’s Labor Management technology team to build machine learning solutions that improve labor forecasting and retail workforce planning. The role develops and maintains forecasting solutions, scalable machine-learning and data pipelines, and production MLOps processes. It works with data scientists, cloud engineers, and business teams to deliver labor and demand forecasting models, while monitoring model and pipeline performance and resolving production issues. The position follows a hybrid work model based in Lakeland, Florida. Candidates must live within commuting distance or be willing to relocate there, and candidates advancing in the interview process must participate in at least one onsite interview. The position applies software engineering practices to create reliable, maintainable, and testable machine-learning solutions.
Requirements
- Bachelor’s degree in computer science or another analytical discipline, or equivalent experience.
- At least 5 years of software, data, or machine-learning engineering experience, including strong experience developing production solutions with Python and SQL.
- At least 2 years of experience with Databricks and Spark/PySpark.
- At least 2 years of experience deploying and supporting machine-learning models in an enterprise production environment.
- Experience building data or machine-learning pipelines involving large datasets and Declarative Asset Bundles (DABs).
- Experience with MLflow, Delta Lake, and Azure.
- Experience with time-series and demand-based forecasting models.
Nice To Haves
- Experience with workforce or labor forecasting models.
- Experience supporting machine-learning solutions in retail, supply chain, workforce management, logistics, or other operational environments.
- Knowledge of MLOps, CI/CD, automated testing, model monitoring, and version control.
Responsibilities
- Develop and maintain machine-learning and forecasting solutions using Python and SQL.
- Build scalable machine-learning and data pipelines using Databricks, Spark/PySpark, and Delta Lake.
- Implement MLOps practices for model training, deployment, versioning, monitoring, and retraining.
- Use MLflow and related technologies to manage the machine-learning lifecycle.
- Develop automated deployment processes using Databricks Asset Bundles (DABs) and CI/CD.
- Partner with data scientists, cloud engineers, and business teams to produce labor and demand forecasting models.
- Monitor model and pipeline performance and troubleshoot production issues.
- Apply software engineering practices to create reliable, maintainable, and testable machine-learning solutions.
Benefits
- Year-end bonus: one month’s extra pay each year for associates who remain employed through the bonus check issue date; prorated in the first year.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
