Role overview
Hybrid
U.S. Citizen, U.S. Person, or Immigration Status Requirements:
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
What you'll work on
- As a Principal Data Scientist, 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 hands-on, high-impact role where your models will not sit on a shelf; they will directly influence real manufacturing outcomes. You will design, deploy, and sustain production-grade machine learning solutions, build intuitive data visualization and statistical analysis tools, and partner closely with stakeholders to turn complex data into clear, actionable insights.
- In addition to technical leadership, you will mentor and guide less experienced data scientists and engineers, helping elevate team capabilities and establish best practices in applied data science. You will act as a technical leader, shaping both solutions and the people delivering them.
- You will become a subject matter expert in factory test data, using that knowledge to uncover opportunities, solve challenging problems, and deliver measurable improvements to production processes. In a fast-paced, multi-tasking environment, you will work directly with customers to ensure data is not just collected, but fully leveraged to improve performance.
- This role offers the opportunity to shape how data science is applied across the organization, contributing to scalable, production-ready solutions and helping set the standard for advanced analytics in manufacturing.
- This is a hybrid role based in Tucson, Arizona.
What we're looking for
- Experience with statistical tools such as Minitab, R, JMP, or SAS.Experience with machine learning pipelines and MLOps practices (e.g., MLflow), including versioning, monitoring, and lifecycle management.
- 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.
Tags & focus areas
Used for matching and alerts on DevFound Fulltime Remote Data Science Ai