Job Summary
This role supports the development and modernization of the demand forecasting capabilities within the client's digital fulfillment organization. The team is responsible for forecasting order volumes, units, and fulfillment capacity across multiple channels (OPU, Ship-to-Home, Drive Up) to optimize store operations planning.
Working closely with data scientists and platform engineers, this role bridges ML research and production by scaling data processing workloads, building robust ML pipelines, and ensuring forecasting models run reliably at scale.
The ideal candidate brings an ML engineering mindset—combining data engineering, pipeline orchestration, and software engineering skills—to modernize a complex forecasting ecosystem that directly impacts store labor planning and customer experience.

Technical Skills: Must Have

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 (table design, partitioning, clustering, 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

Technical Skills: Nice to Have
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)
Familiarity with retail, supply chain, or demand forecasting domains
Experience working with data science teams to productionize research code
Background in scaling ML systems from prototype to enterprise-grade deployments

TECHNICAL SKILLS
Nice To Have
Exposure to ML/analytics-driven systems or forecasting platforms
Advanced performance tuning and scalability optimization experience
Familiarity with retail, merchandising, or supply chain systems
Experience supporting globally distributed teams across time zones
Knowledge of automated alerting, runbooks, and operational playbooks

Notes:
Remote