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About This Role
Job Title: Sr. Data Scientist
Posting Start Date: 6/3/26
Job Location(s): Irvine
If you are looking for a challenging and exciting career in the world of technology, then look no further. Skyworks is an innovator of high\-performance analog semiconductors whose solutions are powering the wireless networking revolution. Through our broad technology expertise and one of the most extensive product portfolios in the industry, we are Connecting Everyone and Everything, All the Time.
At Skyworks, you will find a fast\-paced environment with a strong focus on global collaboration, minimal layers of management, and the freedom to make meaningful contributions in a setting that encourages creative thinking. We are excited about the opportunity to work with you and glad you want to be part of a team of talented individuals who together are changing the way the world communicates.
Requisition ID: 77724
Job Description:Description
Skyworks Solutions is seeking a Machine Learning and Data Engineer to join our rapidly growing AI/ML team in Irvine, CA. This is a highly technical, hands\-on role designed for individuals who thrive at the intersection of cutting\-edge machine learning, data engineering, and semiconductor innovation. If you’re looking to apply your ML expertise to high\-impact, real\-world engineering challenges in the analog and RF domain, this is your opportunity.
Skyworks is an innovator of high\-performance analog semiconductors enabling next\-generation wireless communications. From 5G to IoT to automotive, our technologies are helping to connect the world. At Skyworks, you’ll find a fast\-paced environment with a flat organizational structure and global collaboration. We value open communication, creativity, mutual respect, and technical excellence.
Responsibilities
As a Machine Learning and Data Engineer on the Data Analytics and AI Enablement team, you will lead and contribute to projects that apply advanced ML and deep learning to a variety of use cases across the company—including design automation, yield prediction, anomaly detection, signal classification, and device modeling. You will collaborate with world\-class engineers across electrical, RF, product, and manufacturing domains to deliver real business impact through data and AI.
- Predictive analytics for yield and quality improvement
- Optimization of circuit or device parameters
- RF signal modeling and anomaly detection
- Root\-cause analysis across test and manufacturing data
- Own the end\-to\-end ML pipeline: data acquisition, feature engineering, model selection, training, evaluation, deployment, and monitoring.
- Collaborate with cross\-functional stakeholders including design engineers, process experts, and product owners to understand problem statements and translate them into ML solutions.
- Work on both research\-oriented prototypes and production\-grade deployments.
- Stay up\-to\-date with current trends in ML, especially those relevant to semiconductor, signal processing, or high\-dimensional time\-series data
Required Experience and Skills
- BS and 8 years experience (Ph.D. preferred) in Machine Learning, Computer Science, Electrical Engineering, Applied Mathematics, or a related field.
- Strong theoretical and practical experience in machine learning and deep learning, including CNNs, transformers, time\-series models, or probabilistic methods.
- Proficiency in Python and ML frameworks such as PyTorch, TensorFlow, Scikit\-learn.
- Solid understanding of statistics, optimization, and model evaluation techniques.
- Excellent communication and collaboration skills, with the ability to work across disciplines and teams.
Desired Experience and Skills
- Exposure to semiconductor, electronics, or RF system domains (e.g., basic understanding of circuit behavior, test data, signal integrity).
- Experience working with large, complex datasets (e.g., sensor, waveform, or EDA simulation outputs).
- Familiarity with data infrastructure and workflow orchestration (e.g., Airflow, MLflow, or cloud platforms).
- Contributions to peer\-reviewed research, patents, or open\-source ML projects.
The typical base pay range for this role across the U.S. is currently USD $114,400 \- $220,200 per year. Starting base pay will depend on relevant experience and skills, training and education, business needs, market demands, the ultimate job duties and requirements, and work location. Skyworks has different base pay ranges for different work locations in the U.S. Benefits include access to healthcare benefits (including a premium\-free medical plan option), a 401(k) plan and company match, an employee stock purchase plan, paid time off (including vacation, sick/wellness, parental leave), among others. Employees are eligible to participate in an incentive plan, and certain roles are also eligible for additional awards, including recognition and stock. These incentives and awards are based on individual and/or company performance.
Skyworks is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, age, sex, sexual orientation, gender identity, national origin, disability, protected veteran status, or any other characteristic protected by law. Skyworks strives to create an accessible workplace; if you need an accommodation due to a disability, please contact us at [email protected].
Salary Context
This $114K-$220K range is above the median for Data Scientist roles in our dataset (median: $157K across 236 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 3,823 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Skyworks Solutions, 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 $198,000 based on 808 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($167K) sits 16% below the category median. Disclosed range: $114K to $220K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Skyworks Solutions AI Hiring
Skyworks Solutions has 1 open AI role right now. They're hiring across Data Scientist. Based in Irvine, CA, US. Compensation range: $220K - $220K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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