Data Scientist- Applied AI

Kia Corporation•Tustin, CA

3h•$89,936 - $121,409

About The Position

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.Status Exempt General SummaryThe 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.

Requirements

Bachelor’s degree in a technical or quantitative field required (e.g., Computer Science, Engineering, Mathematics, Statistics, or related field)
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.
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 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
Strong data-driven problem-solving skills
Excellent written and verbal communication skills to coordinate across teams

Nice To Haves

Master’s degree in analytics, data science, or computer science preferred
Familiarity with MLOps concepts (model versioning, deployment workflows, monitoring) preferred.
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 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

Responsibilities

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
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
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
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