Staff Data Science Engineer - Hardware & Silicon Validation

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

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