Staff Data Science Engineer - Hardware & Silicon Validation

Job title: Staff Data Science Engineer - Hardware & Silicon Validation

About the Role Marvell is seeking a highly motivated Data Scientist / Data Analyst to support data analysis and data mining for high-speed DSP 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 to help engineers quickly identify issues, optimize system performance, and improve product quality.

What You'll Do

• 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 We're Looking For

• 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 (Power BI, Superset)
• Experience working with large datasets and performing data cleaning, transformation, and feature engineering
• 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 (Snowflake, PostgreSQL)
• Knowledge of machine learning for anomaly detection, prediction, or optimization
• Familiarity with dashboard design for engineering workflows

Nice to Have

• 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

Compensation & Benefits

• Salary range: $108,220 – $162,100 per year
• Benefits include an employee stock purchase plan with a 2-year look back, family support programs, robust mental health resources, and recognition/service awards