Data Science Engineer

Position: Staff Data Science Engineer
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.

Role Summary

We are looking for a Staff Data Science Engineer to lead the design and delivery of scalable data science, machine learning, and analytics solutions that create measurable business value across the enterprise. This role sits at the intersection of data science, data engineering, analytics engineering, and AI productization. The right person will pair strong technical depth with practical business judgment, helping turn complex data into decisions, tools, and systems that improve operations, reduce cost, accelerate insight, and scale AI adoption.

This is a senior individual contributor role for someone who can operate as a technical leader across functions, influence stakeholders from engineers to executives, and build robust solutions in environments where data quality, governance, speed, and return on investment all matter.

In the current integration environment, this role must also work effectively within approved collaboration and information-sharing processes, including formal handling of cross-company meetings, data requests, documentation, and CSI-sensitive workflows described in the Project Comet guidance.

What You'll Do

* Lead the architecture and implementation of production-grade data science and machine learning solutions, from problem framing through deployment and adoption.

* Build scalable data products, models, and decision-support tools using statistical methods, machine learning, optimization, and modern analytics engineering practices.

* Partner with business leaders, engineering, IT, manufacturing, quality, finance, and other cross-functional teams to identify high-value opportunities and prioritize work with clear business impact.

* Translate ambiguous business problems into structured analytical approaches, measurable success criteria, and deliverable roadmaps.

* Design and maintain reliable data pipelines, feature pipelines, experimentation frameworks, and model monitoring practices.

* Drive the responsible use of AI across the organization by developing reusable frameworks, templates, evaluation approaches, and best practices for enterprise adoption.

* Serve as a technical mentor to data scientists, analysts, and engineers; raise the bar on coding, experimentation, documentation, and stakeholder communication.

* Create executive-ready narratives, visualizations, and recommendations that connect technical findings to business outcomes.

* Partner with data platform and governance teams to ensure solutions meet requirements for security, compliance, and maintainability.

* Help shape standards for model lifecycle management, MLOps, analytics engineering, and AI solution delivery.

* Contribute to integration planning and enterprise analytics initiatives while following approved protocols for meetings, shared materials, data requests, and CSI/non-CSI handling where applicable. Project Comet guidance requires legally approved agendas for certain new cross-company meetings, use of the Data Request List for shared data, and routing potentially sensitive data through the appropriate review path or clean room process.

What Success Looks Like

* You deliver analytics and AI solutions that produce measurable operational or financial impact.

* You help the team focus on high-return opportunities that leadership can easily justify and support.

* You raise technical quality while also improving speed, reuse, and maintainability.

* You make data science more accessible to the business through better tools, communication, and enablement.

* You influence decisions well beyond your direct project work.

* You help the organization…