Machine Learning Engineer (Demand Forecasting - MN)

Dahl Consulting is partnering with a leading national retailer and digital fulfillment organization to identify an experienced Machine Learning Engineer to support the modernization of their demand forecasting capabilities. The role focuses on modernizing and scaling systems that power demand forecasting across a high-volume retail environment.

Responsibilities:

• Scale data processing workloads and convert research-grade code into production-ready, enterprise-scale solutions
• Build, orchestrate, and maintain robust ML pipelines that support forecasting models across multiple fulfillment channels
• Partner with data scientists and platform engineers to productionize models and ensure they run reliably and efficiently at scale
• Optimize data pipelines for performance, scalability, and cost efficiency
• Contribute to the reliability and modernization of a forecasting ecosystem that directly influences store operations planning and customer satisfaction

Requirements:

• Machine Learning & Data Science
• Experience building and deploying ML models in production environments
• Hands-on experience with time series forecasting (Prophet, ARIMA, or similar)
• Understanding of hyperparameter tuning, model validation, and experiment tracking
• Familiarity with feature engineering and feature store concepts
• Data Engineering & Scalability
• Proficiency converting pandas-based workloads to PySpark for large-scale processing
• Experience with distributed data processing frameworks (Spark, Dask, or Ray)
• Ability to optimize data pipelines for performance and cost efficiency
• Working knowledge of data formats (Parquet, CSV) and partitioning strategies
• Experience with BigQuery or similar analytical databases, including table design, partitioning, clustering, and writing/validating datasets
• ML Pipeline Orchestration
• Experience building ML pipelines using Kubeflow Pipelines (KFP), Vertex AI, or Airflow
• Understanding of pipeline component design, DAG orchestration, and caching strategies
• Ability to integrate data validation, model training, and deployment steps into workflows
• Experience with pipeline parameterization and configuration management
• Software Engineering
• Strong Python proficiency with production-grade coding standards
• Ability to read, refactor, and extend existing codebases
• Version control experience (Git) and structured change management
• Familiarity with testing frameworks (pytest), dependency management (Poetry/UV), and code quality tools (pre-commit, linting)
• Cloud & Infrastructure
• Hands-on experience with GCP (Vertex AI, Cloud Storage) or equivalent cloud platforms
• Familiarity with containerization (Docker) and container orchestration (Kubernetes)
• Experience with CI/CD pipelines for ML workflows
• Understanding of secrets management and environment configuration
• Experience with Ray for distributed ML training and inference
• Exposure to Hadoop ecosystem tools (Hive, HDFS, Spark on YARN)
• Knowledge of ML model monitoring and drift detection
• Experience with infrastructure-as-code (Terraform, Cloud Deployment Manager)
• Exposure to ML/analytics-driven systems or forecasting platforms
• Advanced performance tuning and scalability optimization experience
• Familiarity with retail, merchandising, or supply chain systems, including demand forecasting domains
• Experience working with data science teams to productionize research code
• Background in scaling ML systems from prototype to enterprise-grade deployments
• Experience supporting globally distributed teams across time zones
• Knowledge of automated alerting, runbooks, and operational playbooks