Staff Data Science Engineer - Hardware & Silicon Validation

Job Title

Staff Data Science Engineer - Hardware & Silicon Validation

Role Summary

Lead data science and analytics efforts to support hardware/DSP silicon validation. Build scalable data pipelines, apply statistical and machine-learning methods, and deliver dashboards and tools that accelerate debug, improve product quality, and shorten validation cycles.

Experience Level

Senior. Typical background: 3–5 years industry experience with a Bachelor's degree, or 1–2 years with a Master’s/PhD (see Education Requirements for details).

Responsibilities

Design and deliver data infrastructure and analytics to enable hardware validation teams to identify and resolve issues faster.

• Design and implement scalable data pipelines to ingest, process, and store DSP validation and test data.
• Perform statistical analysis and apply machine-learning techniques for pattern recognition, anomaly detection, and root-cause analysis.
• Develop dashboards and visualizations to help AE/FAE and validation engineers interpret test results and debug issues.
• Use cloud-based analytics for efficient large-scale and near-real-time processing.
• Collaborate with hardware, firmware, and validation engineers to define metrics and translate data into actionable insights.
• Automate workflows and build tools that reduce manual debugging effort and accelerate validation cycles.

Requirements

Must-have technical skills and experience; preferred skills listed separately.

• Must-have: Strong foundation in data analysis, statistical modeling, and machine learning.
• Must-have: Proficiency in Python and common libraries (pandas, numpy, matplotlib/seaborn, scikit-learn or similar).
• Must-have: Experience with data visualization tools such as Tableau, Power BI, or equivalents.
• Must-have: Experience working with large datasets, including data cleaning, transformation, and feature engineering.
• Must-have: Experience building or maintaining data pipelines (ETL, batch processing).
• Nice-to-have: Experience with cloud platforms (AWS, Snowflake, Databricks) and cloud-based analytics.
• Nice-to-have: Experience with streaming data, time-series analysis, or signal/data from hardware systems.
• Nice-to-have: Exposure to DSP systems, networking, or semiconductor validation workflows.
• Nice-to-have: Proficiency with SQL and database systems (e.g., Snowflake, PostgreSQL).
• Nice-to-have: Experience applying ML for anomaly detection, prediction, or optimization and designing dashboards for engineering workflows.

Education Requirements

Bachelor’s degree in Computer Science, Electrical Engineering, or a related field with 3–5 years of industry experience; or a Master’s / PhD with 1–2 years of industry experience. Related technical fields are acceptable. No specific certifications were listed.

About the Company

Company: Marvell Technology

Headquarters: Santa Clara, California, United States

Marvell’s semiconductor solutions serve as essential building blocks of the data infrastructure connecting our world, driving innovation across enterprise, cloud, AI, and carrier architectures. The company focuses on creating transformative technology that shapes the future.