Job Description
Qorvo (Nasdaq: QRVO) supplies innovative semiconductor solutions that make a better world possible. We combine product and technology leadership, systems-level expertise and global manufacturing scale to quickly solve our customers' most complex technical challenges. Qorvo serves multiple high-growth segments of large global markets, including consumer electronics, smart home/IoT, automotive, EVs, battery-powered appliances, network infrastructure, healthcare and aerospace/defense. Visit to learn how our innovative team is helping connect, protect and power our planet.
Qorvo’s Internship Program is designed for college students currently enrolled in an accredited Bachelor’s, Master’s, or PhD program. Qorvo offers real work experience, exposure to upper management, and the opportunity to pursue full-time opportunities, as available.
Qorvo’s Internship Program Offers - Challenging, skill-building assignments
- Mentoring and coaching from industry experts
- Launch & Learns and other learning opportunities
- Collaborative team-based work environment
- Networking and social events
- Final presentation to business leaders
Qorvo’s
Data Science Internships are offered in our High Performance Analog, Advanced Cellular, Connectivity and Sensors and Global Operations business groups. Specific projects and responsibilities will be determined based on the business needs at the time of the internship assignment.
Responsibilities May Include - Train, test, and deploy machine learning and AI models in Python, leveraging Spark and/or AWS as needed.
- Analyze large datasets to uncover insights that optimize process and product performance, increase yields, and reduce costs.
- Develop and evaluate predictive, diagnostic, and prescriptive models to support business decisions.
- Implement reproducible research practices including version control, testing, and documentation.
- Collaborate with engineers and data scientists to translate business challenges into data-driven solutions.
Qualifications - Currently pursuing a BS, MS, or PhD in Data Science, Computer Science, Statistics, Applied Math, or related field.
- Minimum GPA of 3.0.
- Proficiency in Python and core data science libraries (pandas, scikit-learn, matplotlib, etc.).
- Experience with at least one deep learning or advanced ML framework (PyTorch, TensorFlow, or Keras).
- Familiarity with Git and SQL.
- Curiosity, strong problem-solving skills, and ability to communicate findings clearly.
Preferred Qualifications - Understanding of Apache Spark or distributed computing.
- Experience with cloud-based environments (AWS, GCP, or Azure).
- Strong presentation and storytelling skills with data.
- Understanding of semiconductor physics fundamentals.
This position is not eligible for visa sponsorship by the Company. Competitive hourly pay commensurate with experience: $31/hr-$42/hr (subject to change dependent on physical location). Posted salary ranges are made in good faith. Qorvo reserves the right to adjust ranges depending on the experience/qualification of the selected candidate as well as external competitiveness and internal comparability. Base compensation is one element of Total Rewards offered at Qorvo. More information on the Total Rewards package can be shared upon request. MAKE A DIFFERENCE AT QORVO
We are Qorvo. We do more than create innovative RF and Power solutions for the mobile, defense and infrastructure markets – we are a place to innovate and shape the future of wireless communications. It starts with our employees. As a unified global team, we bring a commitment to excellence, growth and a passion for creating what's next. Explore the possibilities with us.
We are an Equal Employment Opportunity (EEO) employer and welcome all qualified applicants. Applicants will receive fair and impartial consideration without regard to any characteristics protected by applicable law, including race, color, religion, sex (as defined by law), national origin, age, military or veteran status, genetic information, or disability.
Job Tags
Hourly pay, Full time, Work experience placement,