Data Scientist (Manufacturing/Facilities)

Texas Instruments•Dallas, TX

About The Position

Texas Instruments’ Smart Manufacturing and Automation (SMA) organization is seeking a senior or lead data scientist to improve facilities operations through data. The role combines time-series expertise with enterprise-scale AI deployment, partnering directly with operational teams to analyze complex sensor, equipment, and process data and deliver production-grade anomaly detection and forecasting systems. It also drives adoption of Agentic AI across semiconductor manufacturing, facilities operations, and engineering. The position calls for solving complex operational problems in close collaboration with domain experts, from understanding the problem and exploring data through production deployment. The successful candidate will develop scalable, maintainable analytics and ML systems, establish technical direction for time-series analytics and enterprise AI, lead Agentic AI initiatives, and mentor data scientists and engineers.

Requirements

Master’s degree in Data Science, Computer Science, Mathematics, Statistics, or a related quantitative field.
At least 5 years of experience as a data scientist working with time-series and time-studies data in semiconductor, manufacturing, or facilities domains.
Demonstrated experience deploying enterprise-scale Agentic AI solutions across a large organization.
Deep proficiency in Python and SQL for data processing, feature engineering, and ML model development.
Hands-on experience with time-series methods including anomaly detection, forecasting, and signal processing, such as ARIMA, Prophet, LSTM, Isolation Forest, deep learning, or equivalent.
Experience with MLOps practices, including model versioning, monitoring, and CI/CD for ML pipelines.
Ability to translate complex operational problems into production-grade analytical solutions.
Familiarity with AI-assisted development tools as a productivity accelerator.

Nice To Haves

Ph.D. in Data Science, Computer Science, Mathematics, Statistics, or a related quantitative field.
Track record of leading deployment and scaling of enterprise-wide Agentic AI solutions in a manufacturing or industrial setting.
Familiarity with semiconductor fab operations, facilities systems, equipment maintenance processes, or ESH data.
Experience with CMMS, SCADA, or semiconductor facilities equipment data.
Hands-on experience with agentic frameworks, MCP, A2A, or LLM integration in production systems.
Strong foundation in statistics and experimental design for validating model performance in operational environments.
Excellent communication skills for presenting model insights and AI strategy to technical and non-technical stakeholders at all levels.
Experience training, mentoring, or leading others.

Responsibilities

Engage operational and engineering stakeholders to understand complex time-series data and translate domain expertise into reliable production-grade models.
Develop and deploy anomaly detection, predictive maintenance, and forecasting models using equipment sensor, facilities operations, and manufacturing process data.
Design and implement end-to-end ML pipelines that reduce manual analysis and improve operational decision-making at scale.
Build scalable, maintainable data science solutions using clean architecture, disciplined testing, and rigorous version control.
Design and deploy Agentic AI solutions using frameworks and protocols such as Model Context Protocol (MCP) and agent-to-agent (A2A).
Apply ML engineering practices to ensure models are robust, extensible, and production-grade across distributed manufacturing environments.
Define and drive technical strategy for time-series analytics and enterprise AI deployment, setting standards and identifying platform opportunities.
Lead enterprise-wide Agentic AI initiatives from proof of concept through scaled production deployment across TI’s global manufacturing and facilities organization.
Mentor data scientists and engineers through technical guidance, model reviews, and knowledge sharing.
Proactively identify and initiate improvements beyond assigned scope.