Salaried Deep Learning Jobs in Riverside, CA (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 using data and machine learning to answer complex business questions, build analytical and predictive models, and translate results into clear insights and recommendations for stakeholders. The role goes beyond reporting by framing problems, designing analyses, and influencing decisions.

1st Priority - 30%

Business Problem Framing, Data Wrangling & Analysis  

• Assess the accuracy of new data sources
• Understand business processes and decision frameworks, and translate them into data-driven metrics and KPIs. 
• Preprocess structured and unstructured data
• Analyze large amounts of data to discover trends and patterns
• Build prediction and classification models
• Coordinate with different functional teams for feature engineering
• Partner with business stakeholders to frame problems, define success metrics, and translate business questions into analytical approaches

2nd Priority - 30%

Insight Generation, Visualization & Model Improvement

• Test and continuously improve the accuracy of statistical and machine learning models
• Present insights in a way that clearly ties analysis to business decisions and actions 
• Frame and communicate complex analyses in business-relevant terms that non-technical stakeholders can understand and act on
• Continuously monitor and validate production analysis results

3rd Priority - 20%

Collaborate with IT Team to deploy analysis results

• Build REST APIs for data and analysis result consumption
• Assist the IT system developers to deploy analysis as a service

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 versioning

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

Education:

• Bachelor’s degree in a quantitative field required (e.g., Data Science, Statistics, Computer Science, Engineering, Economics, Mathematics, Business Analytics, or related field)
• Master’s degree in a quantitative field preferred
   

• 3+ years of experience in data science preferred.
• Strong data analysis and statistical foundations required.
• Proficiency in Python and SQL required.
• Familiarity with applied machine learning concepts required.
• Strong business acumen and ability to coordinate between technical teams and non-technical business stakeholders.
• Experience querying databases and using programming languages such as Python and SQL
• Experience using statistics and machine learning algorithms
• 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 of a variety of machine learning techniques, deep learning a plus
• Knowledge of advanced statistical techniques
• 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 big data processing tools such as Spark (e.g., PySpark); experience with Hadoop ecosystem or cloud platforms preferred
• Experience with deep learning frameworks such as PyTorch and TensorFlow preferred
• Strong data-driven problem-solving skills
• Excellent written and verbal communication skills to coordinate across teams