Engineering Data Scientist: Predictive AI & Analytics

WD; San Jose (CA); On-site; USD 140,000 - 187,000; Full time

Job summary

WD is seeking an Engineering Data Scientist to join the AI & Predictive Analytics team, focusing on predictive diagnostic technologies for enterprise HDDs deployed in hyperscale cloud environments.

You will transform more than 10 TB of manufacturing and telemetry data daily into intelligent systems that improve storage reliability and uptime. The role involves building ML models, developing diagnostic algorithms, and deploying analytics into production.

Qualifications

• PhD in CS/Data Science or related engineering field
• Proficient in Python and machine learning techniques
• Experience in data analytics and statistical analysis
• Experience with SQL/NoSQL databases
• Strong analytical problem solving skills

Responsibilities

• Build machine learning models to identify early indicators of drive degradation.
• Develop automated diagnostic algorithms using Python.
• Analyze large-scale telemetry from enterprise storage systems.
• Create data visualization and engineering analysis tools.
• Support root cause investigations using statistical methods.
• Improve existing diagnostic software through automation and refactoring.
• Collaborate with hardware, firmware, and software teams.
• Deploy predictive analytics solutions into production environments.

Skills

Python

Machine learning

Data analytics

Statistical analysis

SQL

NoSQL databases

Analytical problem solving

Education

PhD in CS/Data Science/Engineering

Tools

Hadoop

Hive

Ruby

Front-end development

Web applications

Job description

WD is seeking an Engineering Data Scientist to join the AI & Predictive Analytics team, focusing on predictive diagnostic technologies for enterprise HDDs deployed in hyperscale cloud environments.

You will transform more than 10 TB of manufacturing and telemetry data daily into intelligent systems that improve storage reliability and uptime. The role involves building ML models, developing diagnostic algorithms, and deploying analytics into production.