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About This Role
Job Details:
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Job Description:
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The Advanced Packaging Technology and Manufacturing (APTM) organization is looking for data scientist to support Thermal Compression Bonding (TCB) process control, manufacturing data analytics, yield improvement, and predictive modeling. The successful candidate will work closely with process engineers, equipment engineers, metrology, quality, and manufacturing teams to develop data\-driven solutions that improve process stability, reduce variation, and enhance yield.
Key Responsibilities
- Analyze manufacturing, process, equipment, metrology, and yield data to identify trends, correlations, anomalies, and root causes of process variation.
- Develop and validate data analytics and machine learning models to support: process monitoring, excursion detection, yield prediction, defect pattern analysis and tool health / process drift monitoring
- Build proof\-of\-concepts to demonstrate the technical feasibility of predictive analytics and ML\-based methods for TCB and related manufacturing applications.
- Work with process and equipment teams to define analytics requirements and translate manufacturing problems into scalable data solutions.
- Support advanced process control initiatives
- Collaborate with manufacturing stakeholders to deploy practical solutions that can be used in production environments.
- Develop dashboards, reports, and automated analysis tools to improve decision\-making and reaction time.
- Maintain a strong focus on data quality, model validity, and manufacturing relevance.
Behavioral traits that we are looking for:
- Strong communication and collaboration skills with the ability to work across process, equipment, metrology, quality, and manufacturing teams.
- Proactive and self\-driven, with the ability to work independently and manage multiple priorities.
- Comfortable working in a dynamic manufacturing environment with changing priorities and imperfect data.
- Able to translate complex analytical results into clear actions for non\-data experts.
Qualifications:
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Minimum qualifications are required to be initially considered for this position. Preferred qualifications are in addition to the minimum requirements and are considered a plus factor in identifying top candidates.
Minimum Qualifications
- Bachelor's degree with 6\+ years of relevant experience, or a Master's degree with 4\+ years of relevant experience, or a PhD with 2\+ years of relevant experience in Computer Science, Data Science, Statistics, Electrical Engineering, Industrial Engineering, Mechanical Engineering, or another relevant science or engineering discipline.
- Experience in manufacturing data analysis, statistical analysis, and applying data\-driven methods to solve process or yield problems.
- Strong proficiency in Python programming and JMP for data analysis, automation, and model development.
- Experience working with large datasets from manufacturing, equipment, metrology, or quality systems.
- Demonstrated ability to apply AI/ML, statistical methods, or predictive modeling to extract actionable insights and support business or engineering decisions.
- Knowledge of SQL or other database query tools for data extraction and analysis.
Preferred Qualifications
- Experience in semiconductor manufacturing, preferably assembly / advanced packaging.
- Understanding of advanced process control (APC), including run\-to\-run control, SPC, excursion detection, and process monitoring.
- Experience with yield analysis, process capability improvement, and root cause investigation in a manufacturing environment.
- Familiarity with machine learning models for anomaly detection, prediction, clustering, or classification.
- Experience developing dashboards, reports, or analysis tools.
- Experience working with computer vision or automated inspection data is a plus.
Join us at Intel to contribute to technological advancements while building a rewarding and impactful career. Apply now to be a part of our extraordinary journey.
Job Type:
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Experienced Hire
Shift:
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Shift 1 (United States of America)
Primary Location:
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US, Arizona, Phoenix
Additional Locations:
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Posting Statement:
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All qualified applicants will receive consideration for employment without regard to race, color, religion, religious creed, sex, national origin, ancestry, age, physical or mental disability, medical condition, genetic information, military and veteran status, marital status, pregnancy, gender, gender expression, gender identity, sexual orientation, or any other characteristic protected by local law, regulation, or ordinance.
Position of Trust
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N/A
Benefits
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We offer a total compensation package that ranks among the best in the industry. It consists of competitive pay, stock bonuses, and benefit programs which include health, retirement, and vacation. Find out more about the benefits of working at Intel .
Annual Salary Range for jobs which could be performed in the US: $161,550\.00\-228,070\.00 USD
The range displayed on this job posting reflects the minimum and maximum target compensation for the position across all US locations. Within the range, individual pay is determined by work location and additional factors, including job\-related skills, experience, and relevant education or training. Your recruiter can share more about the specific compensation range for your preferred location during the hiring process.
Work Model for this Role
This role will require an on\-site presence. \* Job posting details (such as work model, location or time type) are subject to change.
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ADDITIONAL INFORMATION: Intel is committed to Responsible Business Alliance (RBA) compliance and ethical hiring practices. We do not charge any fees during our hiring process. Candidates should never be required to pay recruitment fees, medical examination fees, or any other charges as a condition of employment. If you are asked to pay any fees during our hiring process, please report this immediately to your recruiter.
Salary Context
This $161K-$228K range is above the 75th percentile for Data Scientist roles in our dataset (median: $160K across 258 roles with salary data).
View full Data Scientist salary data →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 Intel Corporation, 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. Disclosed range: $161K to $228K.
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.
Intel Corporation AI Hiring
Intel Corporation has 5 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist, AI Software Engineer. Positions span Santa Clara, CA, US, Folsom, CA, US, Phoenix, AZ, US. Compensation range: $153K - $315K.
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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