Data Analyst Associate

2 weeks ago

Newark, California, United States Unigen Full time

Job Title: Data Analyst Associate

Department: Quality

Reports To: Sr. Manager of Quality Assurance Engineering Department

Unigen, headquartered in Newark, California, is a trusted partner for businesses seeking to power their next generation of products. Founded in 1991, we've grown into a leading provider of electronics manufacturing services (EMS), offering a comprehensive suite of solutions from design and manufacturing to supply chain management. From advanced memory modules to high-density storage devices, our solutions are engineered to meet the demanding needs of today's technology landscape. Our commitment to quality and innovation ensures that our partners have the tools they need to succeed.

At Unigen, you'll have the opportunity to work on cutting-edge projects and make a real difference. Working at Unigen means you'll be surrounded by other innovative companies, have access to top talent, and be close to world-class resources. We offer competitive compensation and a comprehensive benefits package, including 401(k) matching. This location, combined with our benefits, provides a unique advantage for those looking to thrive in the semiconductor industry.

Job Summary

The Data Analyst Associate will play a key role in supporting the organization's quality, sustainability, and operational excellence initiatives. This position is responsible for collecting, analyzing, and interpreting diverse data sets—including quality yield, defect rates, RMA (Return Material Authorization) data, customer complaints, inventory, ESG (Environmental, Social, and Governance), GHG (Greenhouse Gas emissions), and production machine data, etc.—to identify trends, patterns, and actionable insights for continuous improvement and compliance.

Key Responsibilities

  • Data Collection & Management:

    Gather, clean, and organize data from multiple sources such as production quality systems, RMA records, customer complaint logs, inventory management systems, ESG, GHG reporting tools, and Production machine data (e.g.: SPI, AXI, AOI, Oven, Pick Placement, Particle counter, Machine down time).
  • Data Analysis:

    Analyze quality data (yield, defect rates), RMA data, customer complaint data, inventory data, ESG metrics, GHG emissions data, and production machine data to identify trends, recurring issues, and patterns for improvement opportunities.
  • Reporting & Visualization:

    Develop, prepare and maintain dashboards, charts, and reports to submit and communicate statuses, findings and trends to stakeholder.
  • Trend & Pattern Identification:

    Use statistical methods and data mining techniques to uncover patterns and correlations that can drive process improvements, reduce defects, reduce machine downtime, and support ESG and sustainability goals.
  • Continuous Improvement Support:

    Collaborate with cross-functional teams (Quality, Production, Customer Service, Inventory, Sustainability) to recommend and implement data-driven improvement initiatives.
  • Documentation:

    Maintain accurate records of analyses, methodologies, and improvement actions taken.
  • Other assignments by the Manager.

Qualifications

  • Bachelor's degree in Statistics, Mathematics, Data Science, Engineering, Computer Engineering, or a related field.
  • 0–2 years of experience in data analysis, preferably in a manufacturing, quality, or sustainability-focused environment.
  • Proficient in data analysis tools such as Excel, SQL, and other data visualization platforms.
  • Detail-oriented with the ability to manage multiple tasks and deadlines

Preferred Skills

  • Experience with statistical analysis software (e.g., R, Python).
  • Familiarity with ESG and GHG reporting frameworks (e.g., GRI, CDP, SASB).
  • Experience with manufacturing data analysis.
  • Familiarity with ERP or MES systems.

Key Performance Indicators (KPIs)

  • Timeliness and accuracy of data analysis and reporting.
  • Number of actionable insights and improvement recommendations generated.
  • Impact of data-driven initiatives on quality, ESG, and training metrics.
  • Responsiveness to ad-hoc data requests from stakeholders.
  • Contribution to sustainability and compliance goals.

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