Manager, Machine Learning Operations

Kohl's

. Attract, retain, develop, manage, coach and assess ML Ops Engineers .

Posted 10/7/2026full-timeMenomonee Falls • Wisconsin • United StatesMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in Machine Learning Engineering and ML Ops practices, with a strong focus on building and scaling ETL pipelines, deploying models, and utilizing cloud infrastructure, particularly Google Cloud Platform. Proven leadership in managing teams, fostering continuous learning, and implementing best practices in data science and analytics.

Highest-signal resume keywords

Machine Learning EngineeringML Ops PracticesGoogle Cloud PlatformCI/CD PipelinesLeadership Experience

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

PythonMachine Learning LibrariesDockerKubernetesSQLSparkKubeflowAirflowAutomated Testing FrameworksOptimization Techniques

Soft Skills

CoachingCommunicationProblem-SolvingTeam ManagementContinuous Learning

Tools & Technologies

Google Cloud Platform ServicesVertex AIBigQueryDataprocGit-based Version ControlAPI Development

Industry Keywords

RetailE-CommerceData ScienceAnalyticsAgile Environments

Tech Stack

Tools & technologies

AirflowBigQueryCloudDockerETLGoogle Cloud PlatformJavaKubernetesPythonPyTorchScikit-LearnSparkSQLTensorflow

About the role

Key responsibilities & impact

Attract, retain, develop, manage, coach and assess ML Ops Engineers
Lead engineers on implementation of the full ML lifecycle, including building and scaling ETL pipelines, deploying Data Scientist-developed models into customer-facing applications, and enabling organizational throughput through cloud infrastructure and tooling
Own the roadmap for Machine Learning Engineering and Data Science tools, including developing reusable frameworks and standardized solutions to streamline model implementation
Research, prototype and instruct the team on creating cutting-edge machine learning infrastructure
Enable change for the team by moving decisions forward and eliminating blockers
Provide technical thought leadership for Data Science managers on cloud-based tools and infrastructure for the ML lifecycle
Instill best practices and foster a culture of continuous learning and development through training, coaching, pair programming and code review
Contribute to development, monitoring, alerting, and automated testing frameworks to ensure reliability, performance, and integrity of data pipelines, models, and infrastructure
Document and communicate implementations and best practices to leaders across Kohl’s broader data organization
Stay current with Google Cloud Platform services and evolving MLE and ML Ops best practices
Perform additional assigned tasks

Requirements

What you’ll need

Bachelor’s degree in Data Science, Computer Science, Statistics, Applied Mathematics or equivalent quantitative field
5+ years of experience as a Machine Learning Engineer with a proven track record of successful, independent, project delivery
2+ years of managerial or leadership experience in Data Science or Analytics organizations
Expertise in ML Ops practices, including building and operating production ML systems using Docker, Kubernetes, CI/CD pipelines, Git-based version control, API development, model serving (batch and real-time), and automated testing frameworks
Experience working with Data Scientists to deploy, scale, and operationalize machine learning models in production environments
In-depth knowledge of cloud platform, preferably 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 working in Agile environments with an emphasis on iterative development and continuous delivery
Proficiency in Java or other languages
Retail and E-Commerce experience
Experience with optimization techniques and tools (e.g., Gurobi, linear programming, mixed-integer programming)
Experience working with agent based or agentic AI systems, including orchestration of autonomous workflows or LLM-driven agents