Data Scientist (Manufacturing/Facilities)

Texas Instruments; Dallas (TX); On-site; USD 120,000 - 190,000; Full time

Job summary

Texas Instruments Incorporated is seeking a Sr./Lead Data Scientist for Smart Manufacturing and Automation in Dallas, TX. You will work with facilities operations teams to extract signals from complex time-series sensor and process data, building production-grade anomaly detection and forecasting systems and driving enterprise AI adoption across TI’s global manufacturing footprint.

You will operate in a complex environment, translating domain knowledge into robust models, and deploying

Qualifications

• Deep time series expertise and enterprise-scale AI deployment.
• Experience with anomaly detection and forecasting in industrial data.
• Ability to deploy intelligent systems at scale in manufacturing environments.

Responsibilities

• Analyze and model time series data with stakeholders to extract value.
• Develop production-grade anomaly detection, forecasting, and predictive maintenance models.
• Design end-to-end ML pipelines that scale and improve operational decisions.

Skills

Time series analysis

Anomaly detection

Forecasting models

ML pipeline development

Production-grade deployment

Mentoring/leadership

Tools

Model Context Protocol (MCP)

Agent-to-Agent (A2A) protocols

Large language model tooling

Job description

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 Do

Analyze 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

• Define and drive technical strategy for time series analytics and enterprise AI deployment - setting standards and identifying platform-level opportunities that compound impact over time
• 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, elevating team capability through technical guidance, model reviews, and knowledge sharing
• Proactively identify and self-initiate improvements beyond assigned scope, bringing a continuous improvement mindset to every model and system you build