Principal Data Scientist – Factory Intelligence

RTX•Tucson, AZ

5d•$107,500 - $204,500•Hybrid

About The Position

As a Principal Data Scientist – Factory Intelligence, you will play a key role in transforming factory data into actionable intelligence that directly impacts production performance. Working across Engineering, Operations, and Quality, you will lead the development and deployment of predictive analytics solutions that improve yield, reduce variation, and drive smarter decision-making at scale. This is a hybrid role based in Tucson, Arizona.

Requirements

Typically requires a University Degree or equivalent experience and a minimum of 8 years of prior relevant experience, or an Advanced Degree in a related field and a minimum of 5 years of experience
Experience developing in Python (NumPy, SciPy, scikit-learn, scikit-image) for production-grade statistical or machine learning applications (beyond academic examples)
Demonstrated experience deploying, maintaining, and scaling machine learning models in production environments
Experience with relational database management and SQL development
U.S. Citizen - Active and transferable U.S. government issued security clearance is required prior to start date.
U.S. citizenship is required, as only U.S. citizens are eligible for a security clearance

Nice To Haves

Experience with statistical tools such as Minitab, R, JMP, or SAS
Strong knowledge of machine learning pipelines and MLOps practices (e.g., MLflow), including versioning, monitoring, and lifecycle management
Strong experience applying quantitative techniques (normalization, standardization, applied statistics) to analyze large, complex datasets and build deployable machine learning models, including in cloud environments such as AWS or Azure
Experience designing, training, fine-tuning, and deploying deep learning models using frameworks such as PyTorch or TensorFlow
Experience working with large language models (LLMs), including fine-tuning, evaluation, and deployment; or demonstrated deep knowledge of LLM concepts and architectures
Experience operating in a technical leadership or mentoring role

Responsibilities

Transform factory data into actionable intelligence that improves production performance.
Collaborate with Engineering, Operations, and Quality teams to build and deploy predictive analytics solutions.
Develop models that directly impact yield, reduce variation, and support smarter decision-making.
Design, deploy, and maintain production-grade machine learning solutions.
Build intuitive data visualization tools and statistical analysis applications.
Partner with stakeholders to translate complex data into clear, practical insights.
Provide technical leadership and mentor junior data scientists and engineers.
Establish best practices in applied data science across the organization.
Become a subject matter expert in factory test data and uncover opportunities for improvement.
Solve challenging, data-driven manufacturing problems and deliver measurable production enhancements.
Work directly with customers to ensure data is fully leveraged to improve performance.
Contribute to scalable, production-ready data science solutions and help advance the organization’s analytics standards.
Operate effectively in a fast-paced, multi-tasking environment.