About the role

Role Overview

As a Machine Learning Engineer II (Remote), you will partner with cross-functional teams to design and implement machine learning-based solutions and help build Machine Learning Engineering (MLE) internal tools and best practices.

Responsibilities

Collaborate with engineers to integrate Data Science models into customer-facing solutions
Support model development via Google Cloud Platform (GCP) service enablement and configuration
Contribute to the roadmap for Data Science and MLE team tools and technology
Design and implement monitoring and alerting to maintain model performance and integrity
Build reusable toolsets to standardize and streamline model implementations
Mentor and guide junior ML engineers and promote continuous learning
Work iteratively across the data science lifecycle: develop, document, and communicate
Partner with Data Scientists to scale ETL workflows and models to production requirements
Optimize and fine-tune models for peak performance and accuracy within resource constraints
Stay current on GCP services and best practices for ML implementations

Requirements

Bachelor’s degree in Data Science, Computer Science, Statistics, Applied Mathematics (or equivalent quantitative field)
3+ years of progressively complex Data Science/analytics experience
3+ years as a Machine Learning Engineer with proven successful delivery
Strong GCP knowledge, especially Vertex AI, BigQuery, and Dataproc
Expertise in CI/CD and Infrastructure as Code (IaC) best practices
Deep understanding of distributed computing / big data technologies: Spark, Kubeflow, Airflow, SQL
Extensive experience with Python and ML libraries: TensorFlow, PyTorch, scikit-learn
Experience with Agile/XP development
Familiarity with OGSM (Objectives, Goals, Strategies, Measures)

Preferred Qualifications

Master’s degree and/or Ph.D.
Proficiency in Java/Scala (or other languages)
Retail and/or e-commerce experience

About Kohl's

Kohl’s is a retail company operating in the e-commerce and omnichannel space. The role sits within their Data Science and Machine Learning Engineering team, focused on building production-grade machine learning solutions and internal tooling on Google Cloud.