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About This Role
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
Qualifications
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
About Us
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
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
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.
TI does not make recruiting or hiring decisions based on citizenship, immigration status or national origin. However, if TI determines that information access or export control restrictions based upon applicable laws and regulations would prohibit you from working in this position without first obtaining an export license, TI expressly reserves the right not to seek such a license for you and either offer you a different position that does not require an export license or decline to move forward with your employment.
Job Info
Job Identification 25016669
Job Category Information Technology
Posting Date 08/10/2026, 08:14 AM
Degree Level Master's Degree
Locations EXSE 13542 N Central Expy, Dallas, TX, 75243, US
ECL/GTC Required Yes
Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 4,317 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Texas Instruments, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $192,890 based on 789 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Texas Instruments AI Hiring
Texas Instruments has 1 open AI role right now. They're hiring across Data Scientist. Based in Dallas, TX, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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