About The Role:

• Is an expert in their craft, creating high quality ML infrastructure and delivering impactful machine learning models to our stakeholders. 
• Works in close collaboration with the other Data Science team members and keeps the business value at the center of their work. Has a bias for action, balancing delivering impact in the short-term while building out the long term vision.
• Applies their ML / MLOPS knowledge to suggest new patterns, tools, approaches to improve the team’s models

• Build reliable, efficient, and scalable infrastructure for our AI/ML capabilities
• Create robust data pipelines to feed analyses and models
• Enable forecasting, network orchestration, and live pricing systems
• Ensure data quality and data integrity through best practices in data integration
• Build out robust feature stores, model orchestration tooling, experimentation tooling, model performance monitoring.
• Create standards and templates for model development and deployment across all Data Science teams.

• Bachelor’s Degree plus at least 3 years of experience in machine learning engineering, or Master’s Degree plus at least 2 years in machine learning engineering
• This experience should include:
  • Developing and optimizing MLOps pipelines for speed, reliability, and observability.
  • Utilizing statistical modeling or machine learning techniques to solve business problems.
  • Strong proficiency in Python and SQL.
  • Hands-on experience with open-source languages and tooling for large-scale ML (e.g., Ray, Flink, Feast).
  • Working with Data Warehouses (e.g., Redshift, Databricks, Snowflake).
  • Utilizing cloud-based (AWS Preferred) data engineering and data science tools.
• Experience building ML systems in Startups is a plus 
• Experience with DS/ML in Logistics/Supply Chain is a plus.