Factory Analytics Data Scientist I (Remote)

Pratt & Whitney•Cedar Rapids, IA

1d•Remote

About The Position

At RTX, the world's largest aerospace and defense company, 185,000 great minds are united by purpose and inspired to make a difference solving the world’s most complex problems. With our three market leading businesses, world-class operations and investments in research and development, we offer capabilities and opportunity no one else can. Together, we push the boundaries of known science and find new ways to connect and protect our world. Collins Aerospace is a leader in technologically advanced, intelligent solutions that help redefine the aerospace and defense industry. With a comprehensive portfolio and deep technical expertise, we help customers meet the demands of the global market. Join us and help shape the future of aerospace and defense. This position is classified as Remote: Employees who are working in Remote roles will work primarily offsite (from home). If you live within a reasonable commute of an RTX site with other colleagues you interact with, your manager will discuss whether there is a degree of onsite presence associated with this role. As part of our commitment to maintaining a secure hiring process, candidates may be asked to attend select steps of the interview process in-person at one of our office locations, regardless of whether the role is designated as on-site, hybrid or remote.

Requirements

Bachelor's degree in Manufacturing Engineering, Electrical Engineering, or related Science, Technology, Engineering, or Mathematics (STEM) major obtained by start date.
Internship or academic experience working with real‑world datasets, ideally involving engineering, manufacturing, or operations data.
Experience in Python, R, or similar analytic programming languages.
Experience working with SQL and relational databases.
Experience may be obtained through on ‑ the ‑ job experience, research, coursework, or projects.

Nice To Haves

Prior experience in a manufacturing or operations environment through internships, co‑ops, or project work.
Experience with Power BI, Tableau, or similar data visualization tools.
Experience with cloud technologies such as Azure, AWS, or GCP.
Experience with SPC, Six Sigma, DOE, or other process improvement methodologies.

Responsibilities

Analyze production, quality, test, and equipment datasets to uncover trends, anomalies, and improvement opportunities.
Build and maintain predictive models supporting yield improvement, scrap reduction, throughput optimization, equipment health monitoring, and process capability.
Create dashboards, analytics pipelines, and reporting tools that provide actionable insights to manufacturing and operations teams.
Partner with operations engineers, quality teams, and frontline staff to understand process needs and turn data findings into real factory impact.
Document and communicate analytical results to support root‑cause investigations, continuous improvement initiatives, and engineering decisions.
Support deployment, monitoring, and troubleshooting of analytical models used in production environments.
Apply statistical process control, experiment design, and data governance standards to ensure reliable and accurate insights.