Data Scientist (Manufacturing/Facilities)

About This Role

Smart Manufacturing and Automation (SMA) at Texas Instruments is looking for a Sr./Lead Data Scientist who can fundamentally change how facilities operations leverage data. This role sits at the intersection of deep time series expertise and enterprise-scale AI deployment . You will work directly with operational teams to extract signal from complex sensor, equipment, and process data, build production-grade anomaly detection and forecasting systems, and drive the adoption of Agentic AI solutions across a global organization spanning semiconductor manufacturing, facilities operations, and engineering.

This role is not about building models in isolation. It involves solving problems in complex operational environments, earning the trust of domain experts, and deploying intelligent systems that scale.

About You

You are an experienced data scientist energized by ambiguous, high-stakes problems. You can walk into a room with facilities engineers and operations specialists, ask the right questions, and leave with a clear picture of what needs to be modeled. You do not just build models - you understand the operational problem deeply enough to know when a simpler statistical approach beats a complex neural network.

You are self-directed. You find anomaly patterns others miss, propose forecasting solutions before anyone asks, and take ownership of outcomes from data exploration through production deployment. You stay current with emerging technologies - including agentic AI frameworks and large language model tooling - not because it is trendy, but because you know how to apply them where they genuinely move the needle in a manufacturing environment.

What You'll DoAnalyze and Model Time Series Data

• Engage directly with operational and engineering stakeholders to understand, map, and extract value from complex time series data - translating domain expertise into reliable, production-grade models
• Develop and deploy anomaly detection, predictive maintenance, and forecasting models against equipment sensor data, facility operations data, and manufacturing process signals
• Design and implement end-to-end ML pipelines that reduce manual analysis effort and increase operational decision quality at scale

Build with Engineering Excellence

• Build scalable, maintainable data science solutions with clean architecture, disciplined testing, and rigorous version control practices
• Design and deploy Agentic AI solutions leveraging emerging frameworks such as Model Context Protocol (MCP) and agent-to-agent (A2A) protocols to create intelligent, composable data analysis workflows
• Apply strong ML engineering fundamentals to ensure models are robust, extensible, and production-grade across distributed manufacturing environments

Lead and Elevate