Machine Learning Engineer II (Remote)

KOHLS•Menomonee Falls, WI

80d•Remote

About The Position

In this role you will support cross-functional teams in designing and implementing machine learning-based solutions and contribute to Machine Learning Engineering (MLE) internal tools and best practices.

Requirements

Bachelor’s degree in Data Science, Computer Science, Statistics, Applied Mathematics or equivalent quantitative field
3+ years of progressively complex Data Science or analytics experience
3+ years of experience as a Machine Learning Engineer with a proven track record of successful project delivery
In-depth knowledge of Google Cloud Platform services, particularly Vertex AI, BigQuery and Dataproc
Extensive expertise with CI/CD and IaC best practices
Extensive knowledge of distributed computing and big data technologies like Spark, Kubeflow, Airflow and SQL
Extensive expertise in Python and machine learning libraries (e.g., TensorFlow, PyTorch, scikit-learn)
Experience with Agile/XP software development
Experience with OGSM approach; strategy development, and execution

Nice To Haves

Master’s Degree and/or Ph.D.
Proficiency in Java/Scala or other languages
Retail experience
E-commerce experience

Responsibilities

Work with Engineers to integrate Data Science models into customer-facing solutions
Support model development through Google Cloud Platform (PCP) service enablement and configuration
Contribute to the roadmap for Data Science and MLE team tools and technology development
Design and implement monitoring and alerting systems to maintain model performance and integrity
Work within the Machine Learning Engineering team to develop leverageable toolsets to standardize and streamline model implementations
Mentor and guide junior machine learning engineers, providing technical expertise and fostering a culture of continuous learning and development
Think in terms of iterative development, document, and communicate throughout the products' data science lifecycle
Work closely with Data Scientists to efficiently scale Extract Transform Load (ETL) workflows and models to meet production requirements
Optimize and fine-tune models to achieve peak performance and accuracy within resource constraints
Stay up-to-date on GCP services and best practices for Data Science and Machine Learning implementations
Additional tasks may be assigned