Data Scientist

Texas Instruments•Dallas, TX

About The Position

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.

Requirements

Experienced data scientist energized by ambiguous, high-stakes problems.
Ability to engage with facilities engineers and operations specialists, ask the right questions, and understand modeling needs.
Understanding of operational problems to determine appropriate modeling approaches (simple statistical vs. complex neural networks).
Self-directed with the ability to find anomaly patterns, propose forecasting solutions, and take ownership of outcomes from data exploration through production deployment.
Knowledge of emerging technologies, including agentic AI frameworks and large language model tooling, and their application in manufacturing environments.
Experience with time series data analysis and modeling.
Experience developing and deploying anomaly detection, predictive maintenance, and forecasting models.
Experience designing and implementing end-to-end ML pipelines.
Experience building scalable, maintainable data science solutions with clean architecture, disciplined testing, and rigorous version control practices.
Experience designing and deploying Agentic AI solutions.
Strong ML engineering fundamentals.
Experience defining and driving technical strategy for time series analytics and enterprise AI deployment.
Experience leading enterprise-wide AI initiatives from proof of concept through scaled production deployment.
Mentorship experience.

Responsibilities

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.