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
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
