Data Scientist

Cherokee Nation Businesses•Catoosa, OK

26d

About The Position

As a Data Scientist at CNE, you will partner with a Senior Data Scientist and Analytics Manager to identify opportunities where AI/ML can create measurable business value. You will design and implement technical solutions across diverse business areas, from building data pipelines and developing models to deploying production-ready systems. This role requires creativity, technical expertise, and strong communication skills to translate ambiguous requirements into actionable insights and scalable solutions.

Requirements

Graduate degree (MS/PhD) or BS with equivalent experience in Statistics, Applied Mathematics, Computer Science, or related fields.
Strong programming skills in Python, PySpark, or R; experience with frameworks such as scikit-learn and PyTorch.
Proficiency in SQL and data manipulation using open-source tools.
Hands-on experience with ML techniques: regression, classification, deep learning, time series, and modern approaches.
Experience building feature pipelines, model training, evaluation, and monitoring.
Familiarity with version control (Git preferred) and MLOps concepts/tools.
Robust analytical and data wrangling skills with a creative approach to problem-solving.
Strong visualization, presentation, and communication abilities.
Ability to work independently as a professional developer, including reviewing code.
Must be able to obtain and maintain licensing by the appropriate gaming authority.

Responsibilities

Develop and maintain organized, version-controlled code bases.
Build reliable data pipelines for feature engineering and data manipulation.
Produce high-quality code beyond model training, ensuring robust deployment.
Translate informal business requirements into clear problem definitions.
Advise leadership on modeling, analytics strategy, and data-driven decision-making.
Design and implement data storage, synthesis, and analysis frameworks.
Review analyses for accuracy, completeness, and adherence to standards.
Support teams throughout all phases of the analytics lifecycle.
Create tools and methods to extract and analyze complex information.
Prepare presentations for executive management and committees.
Stay current through professional education and continuous learning.
Lead or guide the work of other employees when required.