Kia Engineering Jobs (NOW HIRING)

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 at scale. This 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.

1st Priority - 30%

Data Processing and Modeling 

• 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 

2nd Priority - 30%

Model Evaluation, Iteration, and Insight Communication

• 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

3rd Priority - 20%

Collaborate with IT on model deployment and MLOps setup 

• 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

4th Priority - 20%

Clear documentation, source code management, and reproducible analysis

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

• 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 manage projects end-to-end and collaborate across technical and non-technical teams.
• Experience querying databases and using programming languages such as Python and SQL
• Experience using statistics and machine learning algorithms (deep learning a plus)
• Experience with big data processing frameworks such as Spark (e.g., PySpark); experience with Hadoop ecosystem or cloud platforms (e.g., Databricks, AWS) preferred
• Experience publishing results to stakeholders through dashboards (e.g. Power BI, MicroStrategy, Tableau)

• Proficiency in Python and SQL
• Knowledge and experience with NLP and related applied AI techniques (e.g., embeddings, retrieval, GenAI workflows)
• Experience with common Python libraries for data analysis such as Pandas and NumPy
• Experience with visualization libraries such as Matplotlib, Seaborn, Plotly, Bokeh and plotnine
• Experience developing and evaluating statistical and machine learning models using libraries such as statsmodels and scikit-learn
• Experience with deep learning frameworks such as PyTorch and TensorFlow preferred
• Experience with big data processing tools such as Spark (e.g., PySpark); experience with Hadoop ecosystem or cloud platforms preferred
• Strong data-driven problem-solving skills
• Excellent written and verbal communication skills to coordinate across teams