Principal AI Data Scientist, Service - USA Remote

Danaher Corporation

2d•Remote

About The Position

At first glance, you’ll see Danaher’s scale. Our 65,000+ associates work across the globe at more than 15 unique operating companies within three platforms—life sciences, diagnostics, and biotechnology. Look again and you’ll see the opportunity to build a meaningful career, be creative, and take risks with the support you need to be successful. You’ll find associates who are as certain about their impact as they are about where they’re headed next. You’ll find the Danaher Business System, which makes everything possible. You’ll also see a company that investors trust—our culture of continuous improvement has helped us outperform the S&P 500 by more than 5,000% over the past 25 years. The Principal AI Data Scientist – Service will join the Supply Chain AI team, reporting to Director, Supply Chain AI Technology. This role is responsible for designing, developing, and deploying end-to-end AI and data science solutions that power our global service operations—spanning field service engineer (FSE) workforce optimization, service job routing, predictive and preventative maintenance, and service parts inventory management. This role is ideal for senior practitioners who are passionate about building production-grade, cloud-native AI/ML and optimization solutions—not just proofs of concept. You will leverage Azure for compute and agent workflows, and Snowflake as the enterprise data platform, to deliver scalable, reliable, and secure solutions with robust CI/CD. You will also work closely with service leaders, planners, and operational teams to translate business problems into AI solutions that drive measurable impact. This role will be working remotely and expected to travel up to 25-30% for workshop and stakeholder engagement. In this role, you will have the opportunity to: Design, develop, and deploy end-to-end AI and data science solutions for service use cases such as:

Requirements

PhD or Master’s degree in Data Science, Computer Science, Operations Research, Industrial Engineering, Applied Mathematics, or a related quantitative field.
Multiple years of experience (typically 7+ years) in AI, data science, or advanced analytics, with significant experience focused on service, maintenance, field operations, or closely related operational optimization domains.
Strong proficiency in Python and experience with modern ML/AI and optimization frameworks (e.g., scikit-learn, PyTorch, OR-Tools/Gurobi or similar, and optionally LLM/agent frameworks for workflow orchestration).
Demonstrated experience building and deploying production-grade AI/ML or optimization solutions on Azure, including containerized services, APIs, and CI/CD pipelines.
Hands-on experience working with Snowflake (or a similar cloud data platform) for data modeling, SQL development, performance tuning, and integrating analytics/ML workloads.

Nice To Haves

Direct experience in Service Operations, Field Service, Asset Management, or Service Supply Chain functions.
Familiarity with ERP and service management systems (e.g., Oracle, SAP, ServiceMax, Salesforce Field Service, or similar) and how they support service orders, assets, and parts.
Experience with large language models (LLMs) and intelligent agents applied to service workflows, such as technician copilots, knowledge retrieval, or conversational troubleshooting.

Responsibilities

Build, productionize, and maintain ML/OR models and agentic workflows using Azure services (e.g., Azure Functions, Container Apps/Kubernetes, Azure ML, Event-driven architectures) and Snowflake as the core data platform.
Own the full lifecycle from problem framing, data exploration, feature engineering, modeling, and experimentation to deployment, monitoring, and continuous improvement in a CI/CD environment (e.g., Azure DevOps or GitHub Actions).
Collaborate cross-functionally with Service Operations, Field Service, Parts Planning, IT, and product/engineering teams to align technical solutions with operational realities, SLAs, and business KPIs.
Partner with solution architects, data engineers, and platform engineers to define robust data models and data pipelines in Snowflake that support analytics, optimization, and real-time decisioning at scale.
Ensure models and services are robust, secure, and production ready, with clear monitoring, alerting, feedback loops, and retraining strategies.