Data Scientist- Applied AI

At Kia, we’re creating award-winning products and redefining what value means in the automotive industry. It takes a special group of individuals to do what we do, and we do it together. Our culture is fast-paced, collaborative, and innovative. Our people thrive on thinking differently and challenging the status quo. We are creating something special here, a culture of learning and opportunity, where you can help Kia achieve big things and most importantly, feel passionate and connected to your work every day.

Kia provides team members with competitive benefits including premium paid medical, dental and vision coverage for you and your dependents, 401(k) plan matching of 100% up to 6% of the salary deferral, and paid time off. Kia also offers company lease and purchase programs, company-wide holiday shutdown, paid volunteer hours, and premium lifestyle amenities at our corporate campus in Irvine, California.

Exempt

The Data Scientist plays an important role in executing data analysis for Kia North America’s regional subsidiaries (KUS/KCA/KaGA/KMX). Kia’s Big Data Analysis team leverages vast and diverse datasets to drive business improvements and insights. The role requires expertise in statistics, machine learning, and computer science to utilize high-performance compute clusters and perform reproducible analyses s position supports the application of data, analytics, automation, and responsible AI to advance Kia’s business operations.

This role focuses on applying data science and AI techniques to analyze text and other unstructured data, build models, and generate insights that support business decisions. The role contributes to developing new AI- and data-driven products, capabilities, and analytical assets, treating data and models as products that can be used and scaled across the business.

• Assess the accuracy of new data sources
• Understand the relationship between data sources and downstream use cases
• Preprocess structured and unstructured data
• Analyze large amounts of data to discover trends and patterns
• Build, train, and evaluate machine learning and AI models, including modern NLP and GenAI approaches where appropriate.
• Coordinate with cross-functional teams for feature engineering and data integration

• Test and continuously improve the accuracy of statistical and machine learning models
• Present information using Python notebooks and/or dashboards
• Explain model behavior and performance in an intuitive manner to technical and non-technical audiences
• Continuously monitor and validate production model performance
• Treat models and analytical outputs as reusable products or services with clear ownership and quality standards

• Build or contribute to REST APIs for model inference and result consumption
• Partner with IT system developers on model deployment and MLOps best practices to ensure production readiness

• Use git within Git Lab
• Create virtual environments to isolate project dependencies and requirements
• Track model performance and hyperparameter configurations
• Track data and model versioning

This list of essential responsibilities and duties is not exhaustive and may be supplemented and changed as necessary by management.

• Bachelor’s degree in a technical or quantitative field required (e.g., Computer Science, Engineering, Mathematics, Statistics, or related field)
• Master’s degree in analytics, data science, or computer science preferred

• 3+ years of experience in data science preferred.
• Strong foundation in machine learning required.
• Strong Python and SQL skills required.
• Hands‑on experience building AI‑powered features or products (e.g., NLP pipelines, GenAI features, AI agents); strong interest in continuous learning expected.
• Familiarity with MLOps concepts (model versioning, deployment workflows, monitoring) preferred.
• Ability to…