Your Responsibilities

As a Data Scientist for JD Power Products located in Greeneville, TN, you will develop and deploy practical, data‑driven applications and machine learning solutions that improve daily manufacturing operations at John Deere Power Products in Greeneville, Tennessee. This role integrates shop‑floor, inspection, and business data to automate manual work, reduce troubleshooting time, and enable faster, higher‑quality operational decisions across assembly, weld, and paint areas.

Rather than producing static reports, the position builds connected tools and workflows that allow engineers, supervisors, quality, maintenance, and operations teams to interact directly with their data, understand process behavior, and take action. The role combines analytics, machine learning, and application development to deliver reliable, production‑ready solutions that improve throughput, quality, uptime, and operator efficiency in a high‑volume manufacturing environment.

The position works across the full manufacturing lifecycle and partners closely with plant stakeholders to ensure solutions are grounded in real operational needs, scalable, and adopted by users. Additionally you will:

• Partner with Greeneville plant stakeholders to identify operational problems, repetitive tasks, and decision points that can be improved through analytics, automation, or machine learning
• Design, build, and sustain SQL‑based data models integrating data from shop‑floor equipment, inspection systems, production tracking systems, and enterprise applications
• Develop, deploy, and support machine learning and advanced analytics solutions, such as anomaly detection, predictive quality, downtime prediction, and process pattern discovery, using real manufacturing data
• Create plant‑focused tools and applications using Ignition, Power BI, SQL, and Microsoft platforms that provide dashboards, alerts, diagnostics, and interactive views aligned to how users work
• Enable users to explore trends, variation, constraints, and cause‑and‑effect relationships in manufacturing processes, moving beyond basic reporting to actionable insight
• Automate data collection, validation, and reporting workflows to reduce manual data entry and spreadsheet dependency
• Perform data exploration, root cause analysis, and model validation to support continuous improvement and production problem solving
• Collaborate with IT, controls, and automation teams to ensure reliable data connectivity, security, and system performance
• Document data flows, analytics logic, models, and applications to support long‑term maintainability and knowledge transfer within the Greeneville facility
• Measure the effectiveness of deployed solutions through adoption, performance improvement, and operational impact, and continuously refine tools based on user feedback and production changes

VISA Sponsorship is NOT available for this position

What Skills You Need

• 3 or more years of experience in data analytics, data science, or applied machine learning, preferably in an industrial or operational environment
• Strong experience working with SQL‑based data sources and complex, structured datasets
• Demonstrated ability to translate operational problems into deployable, data‑driven solutions
• Ability to work independently while collaborating effectively with cross‑functional manufacturing teams
• Strong communication skills, with the ability to explain analytical concepts to non‑technical audiences

What Makes You Standout

• Experience working with manufacturing, operations, quality, maintenance, or logistics data
• Experience building applications or analytics solutions using Ignition, Power BI, or similar platforms
• Hands‑on experience with machine learning techniques such as anomaly detection, classification, regression, or time‑series analysis
• Experience deploying and sustaining analytics or ML solutions used in daily operations
• Familiarity with shop‑floor systems, industrial data sources, or production environments
• Strong focus on usability, adoption, and measurable operational impact

Education

Ideally you will have a degree or equivalent related work experience in the following:

• Bachelor’s degree in Data Science, Computer Science, Engineering, Statistics, Mathematics, or a related technical discipline.
• A Master’s degree or PhD is preferred.