{"id":3467885,"url":"https://alion.io/job/texas-instruments-data-scientist-manufacturingfacilities","title":"Data Scientist (Manufacturing/Facilities)","company":{"id":36400,"name":"Texas Instruments","domain":"ti.com","url":"https://alion.io/company/texas-instruments","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Oracle","truth_index":{"grade":"B","score":76,"open_postings":195,"ghost_share":0,"stale_share":0.959,"repost_share":0,"time_to_fill_p50_days":31,"computed_at":"2026-11-03T05:54:40Z"}},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Richardson, United States","Dallas, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":104000,"max_usd":197000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":1343},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"A2A","optional":false},{"name":"AI Agents","optional":false},{"name":"Anomaly Detection","optional":false},{"name":"CI/CD","optional":false},{"name":"Clean Architecture","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"Prophet","optional":false},{"name":"Python","optional":false},{"name":"SQL","optional":false},{"name":"Time Series","optional":false},{"name":"LLM","optional":true}],"status":"live","first_seen_at":"2026-10-29T11:56:02Z","employer_posted_date":"2026-10-29","last_verified_at":"2026-11-03T09:48:23Z","board_verified":true,"closed_at":null,"days_open":5,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":5},"description":"About This Role\nSmart 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.\nThis 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.\nAbout You\nYou 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.\nYou 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.\nWhat You'll Do\nAnalyze and Model Time Series Data\nEngage 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\nDevelop and deploy anomaly detection, predictive maintenance, and forecasting models against equipment sensor data, facility operations data, and manufacturing process signals\nDesign and implement end-to-end ML pipelines that reduce manual analysis effort and increase operational decision quality at scale\nBuild with Engineering Excellence\nBuild scalable, maintainable data science solutions with clean architecture, disciplined testing, and rigorous version control practices\nDesign 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\nApply strong ML engineering fundamentals to ensure models are robust, extensible, and production-grade across distributed manufacturing environments\nLead and Elevate\nDefine and drive technical strategy for time series analytics and enterprise AI deployment - setting standards and identifying platform-level opportunities that compound impact over time\nLead enterprise-wide Agentic AI initiatives from proof of concept through scaled production deployment across TI's global manufacturing and facilities organization\nMentor data scientists and engineers, elevating team capability through technical guidance, model reviews, and knowledge sharing\nProactively identify and self-initiate improvements beyond assigned scope, bringing a continuous improvement mindset to every model and system you build\n Minimum Requirements\nMaster's degree in Data Science, Computer Science, Mathematics, Statistics, or a related quantitative field\n5+ years of experience as a Data Scientist working with time series and time studies data within the semiconductor, manufacturing, or facilities domain\nDemonstrated experience deploying enterprise-scale Agentic AI solutions across a large organization\nDeep proficiency in Python and SQL for data processing, feature engineering, and ML model development\nHands-on experience with time series methods including anomaly detection, forecasting, and signal processing (e.g., ARIMA, Prophet, LSTM, Isolation Forest, deep learning, or equivalent)\nExperience with MLOps practices including model versioning, monitoring, and CI/CD for ML pipelines\nDemonstrated ability to translate complex operational problems into production-grade analytical solutions\nFamiliarity with AI-assisted development tools as a productivity accelerator\nPreferred Qualifications\nPh.D. in Data Science, Computer Science, Mathematics, Statistics, or a related quantitative field\nProven track record of leading efforts to deploy and scale enterprise-wide Agentic AI solutions in a manufacturing or industrial setting\nFamiliarity with semiconductor fab operations, facilities systems, equipment maintenance processes, or ESH data\nExperience with CMMS (Computerized Maintenance Management Systems), Supervisory Control and Data Acquisition systems (SCADA), or semiconductor facilities equipment data\nHands-on experience with agentic frameworks, MCP, A2A, or LLM integration in production systems\nStrong foundation in statistics and experimental design for validating model performance in operational environments\nExcellent communication skills for presenting model insights and AI strategy to technical and non-technical stakeholders at all levels\nExperience training, mentoring, leading 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