Data Analyst

We are seeking a Data Analyst to support advanced analytics and machine learning initiatives focused on issue detection, problem definition, and resolution support within a global engineering and quality environment. The role involves working with cross-functional teams across multiple regions to accelerate data-driven decision-making and improve product and field quality performance.

The analyst will apply both traditional and advanced analytics techniques to support end-to-end problem-solving across complex product systems, including high-volume engineering and operational datasets.

Roles and Responsibilities

• Perform data analysis to identify emerging issues, trends, and root causes
• Support advanced analytics and machine learning initiatives for problem detection and resolution
• Develop dashboards and reports to support field quality and engineering decision-making
• Work with structured and unstructured datasets from manufacturing, warranty, and operational systems
• Collaborate with engineering and quality teams across global locations
• Apply statistical and machine learning techniques to improve diagnostic and prediction capabilities
• Support data-driven investigations for complex system and product issues

Required Skills & Experience

• 3+ years of experience in data analysis, preferably in automotive, manufacturing, or industrial domains
• Strong proficiency in SQL, Power BI, and Python (or R)
• Experience working with cloud and big data tools such as Azure, Spark, or Jupyter
• Familiarity with machine learning techniques (e.g., regression, classification, clustering, anomaly detection)
• Experience working with large-scale datasets such as warranty, manufacturing, or telemetry data
• Bachelor’s degree in Engineering, Computer Science, Mathematics, Statistics, or related field
• Strong analytical thinking and problem-solving skills

Preferred Skills

• Experience with predictive analytics and anomaly detection use cases
• Exposure to product quality, reliability engineering, or field failure analysis
• Experience working in global, cross-functional environments

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