Senior Data Systems & Analytics Engineer

ZOLL Medical Corporation•Chelmsford, MA

4d•$135,000 - $170,000•Hybrid

About The Position

The Senior Data Systems & Analytics Engineer is responsible for developing advanced analytics, dashboards, machine-learning driven models, and generative AI-enabled tools that support ZOLL's Global Technical Services function. This role enables data-driven decision making by transforming datasets into actionable insights, predictive models, and intelligent workflows. This role will serve as a technical expert in applied analytics, machine learning, and AI tool development, with a strong focus on delivering insights, optimizing processes, and accelerating innovation across ZOLL. At ZOLL, we're passionate about improving patient outcomes and helping save lives. We provide innovative technologies that make a meaningful difference in people's lives. Our medical devices, software and related services are used worldwide to diagnose and treat patients suffering from serious cardiopulmonary and respiratory conditions. The Acute Care Technology division of ZOLL Medical Corporation develops and delivers innovative lifesaving products and software solutions to EMS, hospital, public safety, and military customers globally. Products include AEDs, trauma kits, ventilators, temperature management solutions, and more. Our dedicated employees take pride in their commitment to improving patient outcomes while delivering world-class customer service. At ZOLL, you won't just have a job. You'll have a career—and a purpose. Join our team. It's a great time to be a part of ZOLL!

Requirements

Bachelor's Degree Data Science, Engineering, Computer Science, Applied Mathematics, Statistics, or a related discipline required
5+ years of experience in analytics engineering, data analysis, machine learning application development, or similar roles required
Strong proficiency in Python for analytics and ML (pandas, numpy, scikit-learn).
Skilled in dashboarding and visualization tools (Power BI, Tableau, or equivalent).
Experience building predictive models and applying ML techniques to real-world operational data.
Familiarity with LLM concepts (embeddings, RAG, prompting, semantic search) and demonstrated interest in GenAI experimentation.
Ability to clean, transform, and prepare datasets for analytics and AI workflows.
Comfort with complex and ambiguous datasets.
Strong communication and data storytelling abilities.
Experience working with engineering, product support, or industrial data environments.
Experience with APIs, basic scripting, or cloud-based data tools.

Nice To Haves

Experience developing dashboards, predictive analytics, or AI-driven tools in an engineering, industrial, or operations environment preferred
Experience working with datasets from enterprise systems (Oracle Cloud, Camstar, Salesforce, ServiceMax, etc.) preferred
Familiarity with Cloud data platforms (AWS, Azure, etc.) is a plus preferred
Medical device or regulated industry experience is a plus preferred

Responsibilities

DATA PREPARATION, ANALYTICS, AND DECISION SUPPORT Work with IT to prepare well-structured datasets and maintainable data transformations to support dashboards, analytics and AI models.
Develop dashboards, visualizations, and analytics models to monitor product failures, service efficiency, operational trends, and customer feedback.
Perform statistical analysis, predictive modeling, and anomaly detection to support engineering and product support decisions.
Translate complex data into actionable recommendations through clear data storytelling.
Develop clear, well-documented definitions of KPIs, metrics, and analytical logic.
Adhere to data privacy, cybersecurity, and internal governance standards when handling service, customer, or field data.
MACHINE LEARNING AND GENERATIVE AI DEVELOPMENT Apply machine learning techniques (regression, clustering, anomaly detection, forecasting) to derive insights from service and product datasets.
Prepare and structure data for LLM workflows including embeddings, semantic search, summarization, and retrieval-augmented generation (RAG).
Experiment with emerging AI and GenAI tools, prototyping new techniques that enhance automation, documentation, and storytelling for decision support.
Support AI model evaluation, accuracy assessment, and guardrail development for use in regulated environments.
Partner with software developers to integrate ML/AI components into internal tools, assistants, and automation solutions.
Ensure analytic methods and models follow traceability, reproducibility, and documentation expectations suitable for regulated environments.
CONTINUOUS IMPROVEMENT AND INNOVATION Identify and recommend opportunities for automation, advanced analytics, and AI-driven enhancements to business processes.
Research and meet with 3rd party technology vendors to assess applicability to ZOLL processes.
Stay current with emerging AI, ML, and analytics technologies, and evaluate their applicability to current operational challenges.