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.
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:Experienced HireShift:Shift 1 (United States of America)Primary Location: US, Arizona, PhoenixAdditional Locations:Posting Statement: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 TrustN/ABenefits
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.*
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.Skills Required
- Bachelor's degree with 6+ years, Master's with 4+ years, or PhD with 2+ years in relevant field (Computer Science, Data Science, Statistics, EE, IE, ME, or similar).
- 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.
- Knowledge of SQL or other database query tools for data extraction and analysis.
- 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 manufacturing.
- 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.
Intel Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Intel and has not been reviewed or approved by Intel.
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Parental & Family Support — Family-building and caregiving supports are extensive, including fertility coverage, adoption assistance, paid parental leave, childcare and elder care resources, and a structured reintegration for new parents. These benefits are positioned as best-in-class elements of the package.
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Leave & Time Off Breadth — Time off includes generous PTO, a paid sabbatical after extended tenure, and multiple leave types such as family, medical, bereavement, and military. This breadth enables employees to disconnect, recharge, and manage life events.
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Retirement Support — Long-term savings are bolstered by a competitive 401(k) match and access to deferred compensation for eligible levels, alongside stock purchase opportunities. These programs are highlighted as strong tools for financial security.
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