Manager – Data Science & MLOps

Milwaukee Tool•Menomonee Falls, WI

About The Position

At Milwaukee Tool we firmly believe that our People and our Culture are the secrets to our success -- so we give you unlimited access to everything you need to provide support to your business unit. Our people and our culture are the secrets to our success. We empower you to own it, drive it, and do what it takes to support the biggest breakthroughs in the industry. Applicants must be authorized to work in the U.S.; Sponsorship is not available for this position. Your Role on Our Team: As the Manager – Data Science & MLOps at Milwaukee Tool, you will lead the strategy, delivery, and evolution of our enterprise machine learning capabilities. You will manage and grow a team of data scientists while owning the design, deployment, and governance of MLOps platforms across Databricks, Azure, Citrine, and AWS. This role balances hands-on technical leadership with people management and executive-facing responsibility, empowering teams across the organization to develop, deploy, and operate machine learning solutions at scale. You will shape how data science and MLOps are practiced across the company, setting standards, driving adoption, and ensuring reliable, secure, and impactful outcomes from experimentation through production.

Requirements

7+ years of experience across Data Science, MLOps, DataOps, DevOps, or backend engineering, with demonstrated progression into technical leadership.
Experience managing and mentoring data scientists and/or ML engineers, including setting modeling strategy and delivery expectations.
Deep hands-on experience with Databricks ML services, Azure ML, and AWS/SageMaker in production environments.
Strong Python skills and practical experience with ML frameworks and tooling (e.g., PyTorch, MLflow).
Proven ability to design and implement enterprise-grade MLOps architectures, including model registries, CI/CD pipelines, and automated retraining workflows.
Experience with infrastructure-as-code and automation tooling (Terraform, Spacelift, GitHub Actions, Databricks Asset Bundles, Azure Pipelines).
Strong understanding of containerization and orchestration (Docker, Kubernetes).
Hands-on experience implementing model, data, and system monitoring, including quality, drift, reliability, and cost visibility.
Solid understanding of the end-to-end ML lifecycle, from data ingestion and feature engineering through deployment strategies and long-term maintenance.
Experience operating in multi-cloud environments and partnering effectively across IT, engineering, and business teams.
Experience supporting end user–facing ML solutions, including scenarios where access to data or deployed models may be constrained.

Nice To Haves

Experience leading or supporting GenAI / LLMOps initiatives, including prompt management, evaluation, and deployment patterns.
Knowledge of security, compliance, and governance in regulated or safety-critical environments.
Experience deploying ML models to edge or embedded systems, including familiarity with C/static datatypes and constrained environments.
Familiarity with Citrine, data governance platforms, lineage, and metadata management tools.
Experience with performance testing, observability, and cost optimization for large-scale ML workloads.
Familiarity with transformer-based architectures and LLM frameworks (e.g., Hugging Face, OpenAI, LangChain), including prompt orchestration and agent-based workflows.
Demonstrated ability to scale teams, platforms, and impact—positioning this role for progression into a Senior Manager level.

Responsibilities

Lead and scale a Data Science & MLOps team, initially managing a Senior Data Scientist and Staff Data Scientist, with the ability to augment delivery through contractors and future full-time hires.
Own the enterprise MLOps and applied data science strategy, defining how machine learning is developed, deployed, governed, and operated across the company.
Architect, implement, and evolve scalable MLOps platforms across Databricks, Azure, and AWS to support both centralized IT solutions and distributed domain teams.
Define and enforce architecture standards, tooling, and best practices for model development, versioning, reproducibility, deployment, and lifecycle management.
Build and oversee CI/CD/CT pipelines for model training, validation, deployment, monitoring, and retraining at enterprise scale.
Partner with data scientists on modeling strategy and delivery, ensuring the team’s data science work drives measurable business outcomes (e.g., promo pod and other strategic initiatives).
Collaborate closely with ML engineers, data scientists, electrical engineers, product teams, and DevOps to integrate models into production systems, including cloud, edge, and embedded environments.
Establish and maintain observability and reliability standards for ML systems, including monitoring for model performance, drift, latency, cost, and system health.
Lead governance across the ML lifecycle, working with the broader ML community to define and enforce security, compliance, privacy, and auditability standards—adapting rigor based on use case (experimental, regulated, or productized).
Serve as the executive-facing owner of the ML platform roadmap, clearly communicating strategy, progress, risks, and value to VPs and senior leaders.
Drive enterprise adoption of ML tools and platforms through documentation, training, internal community engagement, and hands-on enablement.
Continuously evolve ML Ops capabilities to support GenAI/LLMOps use cases, balancing innovation with guardrails and responsible usage.
Maintain a hands-on technical presence (~50%), contributing to architecture, design reviews, and complex problem-solving while coaching the team and setting direction.