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
Why TI?
Engineer your future. We empower our employees to truly own their career and development. Come collaborate with some of the smartest people in the world to shape the future of electronics.
We're different by design. Diverse backgrounds and perspectives are what push innovation forward and what make TI stronger. We value each and every voice, and look forward to hearing yours. Meet the people of TI (https://edbz.fa.us2.oraclecloud.com/hcmUI/CandidateExperience/en/sites/CX/pages/4012)
Benefits that benefit you. We offer competitive pay and benefits designed to help you and your family live your best life. Your well-being is important to us. Please find our country-specific benefits here (https://careers.ti.com/en/sites/CX/pages/benefits)
About Texas Instruments
Texas Instruments Incorporated (Nasdaq: TXN) is a global semiconductor company that designs, manufactures and sells analog and embedded processing chips for markets such as industrial, automotive, data center, personal electronics and communications equipment. At our core, we have a passion to create a better world by making electronics more affordable through semiconductors. This passion is alive today as each generation of innovation builds upon the last to make our technology more reliable, more affordable and lower power, making it possible for semiconductors to go into electronics everywhere. Learn more at TI.com .
Texas Instruments is an equal opportunity employer and supports a diverse, inclusive work environment. All qualified applicants will receive consideration for employment without regard to race, color, religion, creed, disability, genetic information, national origin, gender, gender identity and expression, age, sexual orientation, marital status, veteran status, or any other characteristic protected by federal, state, or local laws.
If you are interested in this position, please apply to this requisition.
Minimum Requirements
Master's degree in Data Science, Computer Science, Mathematics, Statistics, or a related quantitative field
5+ years of experience as a Data Scientist working with time series and time studies data within the semiconductor, manufacturing, or facilities domain
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 (e.g., ARIMA, Prophet, LSTM, Isolation Forest, deep learning, or equivalent)
Experience with MLOps practices including model versioning, monitoring, and CI/CD for ML pipelines
Demonstrated ability to translate complex operational problems into production-grade analytical solutions
Familiarity with AI-assisted development tools as a productivity accelerator
Preferred Qualifications
Ph.D. in Data Science, Computer Science, Mathematics, Statistics, or a related quantitative field
Proven track record of leading efforts to deploy and scale 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 (Computerized Maintenance Management Systems), Supervisory Control and Data Acquisition systems (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, leading others
ECL/GTC Required: Yes
