Staff Data Science Engineer - Hardware & Silicon Validation at Marvell Technology

Santa Clara, California, United States -

Full Time

Start Date

Immediate

Expiry Date

26 Sep, 26

Salary

162100.0

Posted On

28 Jun, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, Data Analysis, Machine Learning, Statistical Modeling, Tableau, Data Pipeline Development, ETL, SQL, Pandas, Numpy, Scikit-learn, Cloud Analytics, Anomaly Detection, Feature Engineering, DSP Validation, Data Visualization

Industry

Semiconductor Manufacturing

Description

About Marvell Marvell’s semiconductor solutions are the essential building blocks of the data infrastructure that connects our world. Across enterprise, cloud and AI, and carrier architectures, our innovative technology is enabling new possibilities. At Marvell, you can affect the arc of individual lives, lift the trajectory of entire industries, and fuel the transformative potential of tomorrow. For those looking to make their mark on purposeful and enduring innovation, above and beyond fleeting trends, Marvell is a place to thrive, learn, and lead. Your Team, Your Impact The existing and upcoming megatrends of cloud services, video streaming, 5G wireless and AI/ML among others, are driving the relentless demand for higher bandwidth, lower power and smaller footprint. Marvell offers a field proven solution for high-speed optical interconnects and transceivers that are utilized for a wide array of enterprise, carrier, small medium business, industrial and cloud data center applications. What You Can Expect Key Responsibilities Build Data Pipelines: Design and develop scalable data pipelines to ingest, process, and store large volumes of DSP validation and test data Data Analysis & Modeling: Apply statistical analysis and machine learning techniques to identify patterns, detect anomalies, and support root-cause analysis Visualization & Dashboarding: Develop intuitive dashboards and visualizations to enable AE/FAE and validation engineers to quickly interpret test results and debug issues Cloud-Based Analytics: Leverage cloud technologies to process and analyze large-scale datasets efficiently, enabling near real-time insights Collaboration with Engineering Teams: Work closely with hardware, firmware, and validation engineers to understand data, define metrics, and translate complex data into actionable insights Automation & Efficiency: Build tools and workflows that reduce manual debugging effort and accelerate validation cycles What Makes This Role Exciting Work on cutting-edge high-speed connectivity systems (DSP/PHY) Apply AI/ML to real-world hardware validation challenges Build end-to-end data platforms (from ingestion → analytics → visualization) Direct impact on product quality and time-to-market Opportunity to contribute to next-generation AI-driven debugging platforms What We're Looking For We are seeking a highly motivated Data Scientist / Data Analyst to support data analysis and data mining for high-speed DSP (Digital Signal Processing) validation and interoperability testing. This role focuses on building scalable data pipelines, developing intelligent analytics, and delivering actionable insights to accelerate debug and validation cycles. You will work at the intersection of hardware systems, large-scale data, and AI-driven analytics, enabling engineers to quickly identify issues, optimize system performance, and improve product quality. Minimum Qualifications Bachelor’s degree in Computer Science, Electrical Engineering, or related field with 3–5 years of industry experience, or Master’s / PhD with 1-2 years of experience Strong foundation in data analysis, statistical modeling, and machine learning Proficiency in Python (pandas, numpy, matplotlib/seaborn, scikit-learn or similar) Experience with data visualization tools such as Tableau or equivalent (e.g., Power BI, Superset) Experience working with large datasets and performing data cleaning, transformation, and feature engineering Preferred Qualifications Experience with cloud platforms (e.g., Amazon Web Services, Snowflake, Databricks) Familiarity with data pipeline development (ETL, streaming, batch processing) Experience with time-series data analysis or signal/data from hardware systems Exposure to DSP systems, networking, or semiconductor validation workflows Experience with SQL and database systems (e.g., Snowflake, PostgreSQL) Knowledge of machine learning for anomaly detection, prediction, or optimization Familiarity with dashboard design for engineering workflows #LI-TM1 Expected Base Pay Range (USD) 108,220 - 162,100, $ per annum The successful candidate’s starting base pay will be determined based on job-related skills, experience, qualifications, work location and market conditions. The expected base pay range for this role may be modified based on market conditions. Additional Compensation and Benefit Elements Marvell is committed to providing exceptional, comprehensive benefits that support our employees at every stage - from internship to retirement and through life’s most important moments. Our offerings are built around four key pillars: financial well-being, family support, mental and physical health, and recognition. Highlights include an employee stock purchase plan with a 2-year look back, family support programs to help balance work and home life, robust mental health resources to prioritize emotional well-being, and a recognition and service awards to celebrate contributions and milestones. We look forward to sharing more with you during the interview process. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability or protected veteran status. Any applicant who requires a reasonable accommodation during the selection process should contact Marvell HR Helpdesk at TAOps@marvell.com. Interview Integrity To support fair and authentic hiring practices, candidates are not permitted to use AI tools (such as transcription apps, real-time answer generators like ChatGPT or Copilot, or automated note-taking bots) during interviews. These tools must not be used to record, assist with, or enhance responses in any way. Our interviews are designed to evaluate your individual experience, thought process, and communication skills in real time. Use of AI tools without prior instruction from the interviewer will result in disqualification from the hiring process. This position may require access to technology and/or software subject to U.S. export control laws and regulations, including the Export Administration Regulations (EAR). As such, applicants must be eligible to access export-controlled information as defined under applicable law. Marvell may be required to obtain export licensing approval from the U.S. Department of Commerce and/or the U.S. Department of State. Except for U.S. citizens, lawful permanent residents, or protected individuals as defined by 8 U.S.C. 1324b(a)(3), all applicants may be subject to an export license review process prior to employment. #LI-SA1 Join our talent community to hear about company news, job openings and events. Join our Talent Community! Marvell’s semiconductor solutions are the essential building blocks of the data infrastructure that connects our world. Across enterprise, cloud and AI, automotive, and carrier architectures, our innovative technology is enabling new possibilities. At Marvell, you can affect the arc of individual lives, lift the trajectory of entire industries, and fuel the transformative potential of tomorrow. For those looking to make their mark on purposeful and enduring innovation, above and beyond fleeting trends, Marvell is a place to thrive, learn, and lead. Recruitment fraud is a well-known way that third parties try to get personal information or to steal money from you. Please review Marvell’s guidance here to learn more on how you can protect yourself.

Responsibilities

Design and develop scalable data pipelines to process DSP validation and test data. Apply statistical analysis and machine learning to create dashboards and actionable insights for hardware engineering teams.