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Intac Solution Linkedin · Posted 5mo ago

Data Analyst 2 (Korean Bilingual)

Santa Ana, California, United States

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Indexed description

Job Title: Logistics Risk Analyst

Clint: Samsung

Pay rate: $50-$52

Work Location: 3 MacArthur Place, Santa Ana, CA, USA

Work Schedule: Fully onsite

  • Bilingual Korean not required but strongly preferred

Education and Years of Experience:

  • Bachelor’s degree in Data Analytics, Supply Chain, Engineering, Statistics, or related field.
  • 3+ years of experience in logistics analytics, operations analytics, or supply chain data analysis.

Top Skills:

  • Excel(Pivot, Lookup, data matching & compare),
  • Communication
  • Analysis

Role Summary

Logistics Risk Analyst focuses on data-driven identification, analysis, and monitoring of logistics risks. This role serves as the analytical engine of the logistics risk management function, transforming operational data into actionable insights.

Key Responsibilities

  • Analyze logistics data from TMS, WMS, ERP, and claims systems to identify risk patterns and anomalies.
  • Develop and maintain logistics risk dashboards, KPIs, and early-warning indicators.
  • Track trends in loss, damage, delay, and service failures.
  • Support root cause analysis with quantitative evidence and data modeling.
  • Prepare regular risk reports and ad-hoc analyses for management review.
  • Partner with IT and operations teams to improve data quality and system integration.
  • Support audits, compliance reviews, and risk assessments with data analysis.
  • Recommend process improvements based on data findings.

Qualifications:

  • Bachelor’s degree in Data Analytics, Supply Chain, Engineering, Statistics, or related field.
  • 3-6 years of experience in logistics analytics, operations analytics, or supply chain data analysis.
  • Hands-on experience with Excel, SQL, and BI tools (Power BI, Tableau, etc.).
  • Familiarity with TMS/WMS and logistics performance metrics.
  • Experience handling large datasets and building dashboards.

Core Competencies

  • Strong analytical and quantitative skills
  • High attention to detail and data accuracy
  • Ability to translate data into business insights
  • Structured analysis
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