Manager, Machine Learning Operations (Remote)
At a glance
Manager-level Machine Learning Engineering role at Kohl's. Remote, United States · full-time · no salary stated · we estimate $142,875–$228,150 across the United States.
Kohl's has not published a salary for this role. The figure below is our estimate, not theirs.
aimlroles summary, based on the employer's posting.
What you'll do
- Lead engineers building ETL pipelines, moving data-science models into customer-facing applications, and supporting the full model lifecycle
- Set the roadmap for ML and data-science tools, creating reusable frameworks that make model implementation more consistent
- Manage, coach and assess ML Ops engineers, using pair programming and code review to build skills and team practices
- Improve pipeline and model reliability with monitoring, alerts and automated tests, and share implementation guidance with data leaders
- Research new ML infrastructure, guide cloud tooling decisions and clear blockers that slow the team
What you bring
- A bachelor's degree in a quantitative field such as computer science, statistics or data science, or an equivalent background
- At least five years as a machine learning engineer, delivering projects independently, plus two years leading data science or analytics teams
- Hands-on expertise operating production ML systems, including Docker, Kubernetes, CI/CD, model serving and automated testing
- Strong Python and machine learning library skills, with experience deploying models alongside data scientists and working with cloud platforms
Who this fits
This is a remote manager role for someone who can lead engineers while remaining close to ML infrastructure, deployment and operations. It suits a candidate who has led people in data science or analytics and can set technical direction across the ML lifecycle. A master's degree, retail or e-commerce experience, and work with agentic AI are preferred rather than required.
From the employer
Role Specific Information
Job Description
About the Role
As Manager, Machine Learning Operations (ML Ops), you will manage a team of engineers in designing, deploying, and operating machine learning solutions while building scalable infrastructure, tools, and best practices across the Machine Learning Engineering (MLE) ecosystem.
What You’ll Do
- Attract, retain, develop, manage, coach and assess ML Ops Engineers
- Lead engineers on implementation of the full ML lifecycle, including building and scaling extract, transform, and load (ETL) pipelines, deploying Data Scientist developed models into customer-facing applications, and enabling efficient 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 the topics of cloud-based tools and infrastructure for the ML lifecycle
- Instill best practices for your team and foster a culture of continuous learning and development, while training and coaching them through pair programming and code review
- Contribute to development, monitoring, alerting, and automated testing frameworks to ensure the reliability, performance, and integrity of data pipelines, models, and infrastructure
- Document, and communicate implementations and best practices to other leaders across Kohl’s broader data organization
- Stay current with Google Cloud Product (GCP) services and evolving best practices in MLE and ML Ops
- Additional tasks may be assigned
What Skills You Have
Required
- 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
Preferred
- Master’s Degree
- Proficiency in Java or other languages
- Retail and E-Commerce experience
- 7+ years of experience in Machine Learning
- 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
Essential Functions
The requirements listed below are representative of functions you will be required to perform, however you may be required to perform additional functions. Kohl’s may revise this job description at any time. To perform this job successfully, you must be able to perform each essential function satisfactorily. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions, absent undue hardship.
- Ability to perform the accountabilities listed in the “What You’ll Do” Section
- Ability to comply with dress code requirements
- Basic math and reading skills, legible handwriting, and basic computer operation
- Ability to maintain prompt and regular attendance and meet scheduling requirements as set by the company
- Ability to learn and comply with all company policies, procedures, standards and guidelines
- Ability to give direction and to receive, understand and proactively respond to direction from leadership and other company personnel
- Ability to work as part of a team and interact effectively and appropriately with others
- Ability to maintain composure and work in a fast paced environment while accomplishing multiple tasks within established timeframes
- Ability to satisfactorily complete company training programs
- Ability to use a personal computer for tasks such as communicating, preparing reports, etc.
- Ability to plan, prioritize and monitor activities across business units
- Ability to complete or oversee the completion of assigned projects in a timely manner