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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