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About This Role
Job Title: Principal Data Scientist Posting Start Date: 9/22/26 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: 78473
Job Description: Description
We are seeking a highly motivated Senior Data Scientist to join our team and drive data-driven decision making across engineering, operations, and business functions. In this role, you will leverage advanced analytics, machine learning, statistical modeling, and large-scale data processing to transform complex datasets into actionable insights that improve products, processes, and business outcomes.
The ideal candidate is passionate about solving challenging problems, working with diverse data sources, and partnering with cross-functional stakeholders to deliver innovative analytical solutions. You will play a key role in designing and implementing scalable data science solutions, developing predictive models, and communicating insights that influence strategic and operational decisions.
Responsibilities
Lead end-to-end data science initiatives from problem definition through deployment, adoption, and value measurement. Develop machine learning, statistical, optimization, and AI solutions for complex engineering and business challenges. Analyze large-scale structured and unstructured data to generate actionable insights and recommendations. Design experiments, predictive models, forecasting systems, and anomaly-detection solutions. Build scalable data pipelines, automated workflows, analytical tools, and reusable data products. Create dashboards and visualizations that enable timely, data-driven decisions. Communicate technical findings and strategic recommendations to stakeholders and leadership.
Required Experience and Skills
Bachelor's degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, Operations Research, or a related field.
8+ years of experience in Data Science, Machine Learning, Analytics, or a related field.
Solid understanding of statistics, experimental design, hypothesis testing, and predictive modeling.
Proven track record of delivering data-driven solutions that generated measurable business impact.
Experience developing, validating, and deploying machine learning and statistical models, including Regression, Classification, Clustering, Forecasting, Anomaly Detection.
Experience working with large-scale structured and unstructured datasets.
Experience extracting, transforming, integrating, and analyzing data from multiple disparate sources.
Experience collaborating with cross-functional stakeholders to translate business requirements into analytical solutions.
Strong problem-solving and analytical skills.
Ability to influence decision-making through data insights and recommendations.
Excellent written, verbal, and presentation skills.
Desired Experience and Skills
Preference for Master's degree or PhD in Data Science, Computer Science, Statistics, Mathematics, Engineering, Operations Research, or a related field.
Experience leading end-to-end data science projects from problem definition through deployment and business adoption.
Experience defining analytics strategy and influencing organizational direction.
Experience mentoring junior data scientists, analysts, or engineers.
Experience in semiconductor, manufacturing, quality, supply chain, product, or engineering analytics.
Experience applying analytics to Yield improvement, Process optimization, Quality improvement, Forecasting, Capacity planning, Root-cause analysis, Anomaly detection.
Experience with cloud and big data platforms such as Azure, AWS, Databricks, or Spark.
Experience with Generative AI, Large Language Models (LLMs), and AI-assisted analytics solutions.
Expertise in deep learning frameworks such as TensorFlow or PyTorch.
Knowledge of optimization methods, causal inference, advanced forecasting techniques, or reinforcement learning.
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The typical base pay range for this role across the U.S. is currently USD $122,900 - $255,200 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].
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Salary Context
This $122K-$255K range is above the median for Data Scientist roles in our dataset (median: $167K across 150 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 8,943 AI roles we're tracking, Data Scientist positions make up 4% 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 $190,000 based on 1,406 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. Disclosed range: $122K to $255K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
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: $255K - $255K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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