Quality Data Scientist - Electrical Systems & Field Quality Engineering

Are you passionate about transforming complex data into actionable insights that improve product quality and customer satisfaction?

We are seeking a strategic, hands-on Data Scientist to support Warranty Analytics and Programs within our North America quality team.

The ideal candidate combines advanced data analytics, predictive modeling, and data manipulation expertise with the ability to communicate complex findings through clear, executive-ready PowerPoint presentations.

You will be part of a talented team developing scalable analytics, predictive models, and early-warning capabilities to detect emerging quality trends across large enterprise datasets.

This is a fast-paced environment focused on rapid, high-quality delivery for business partners.

You will work in a highly collaborative organization that values analytical rigor, speed, innovation, and measurable business impact.

• Lead and coordinate cross-functional analytics and AI initiatives from problem definition through deployment, ensuring alignment with business objectives, timelines, and measurable outcomes.
• Perform deep exploratory, diagnostic, and predictive analysis to identify patterns, relationships, anomalies, emerging risks, and leading indicators within complex datasets.
• Build, validate, and deploy predictive models to forecast warranty claims, component failure rates, quality trends, repair demand, and cost exposure.
• Apply statistical analysis, machine learning, forecasting, segmentation, and anomaly-detection techniques to solve business and operational problems.
• Manipulate, integrate, cleanse, transform, and analyze large structured and unstructured datasets from multiple enterprise sources using Python, SQL, PySpark, and related tools.
• Develop robust, repeatable analytical workflows and reusable data products that support scalable decision-making and consistent results.
• Partner with business stakeholders to define analytical questions, translate business needs into actionable use cases and technical requirements, and establish success criteria.
• Collaborate with data scientists, data engineers, platform teams, and subject-matter experts to improve data accessibility, model performance, scalability, and adoption.