Skills

agentic-ai
ai
data-engineering
data-quality
data-science
databricks
machine-learning
python
spark

Full description

Role Summary:

Partners with the Data Engineering and Agentic AI streams to develop and validate the data science/ML components feeding into the client's AI use cases.

Key Responsibilities:

Develop and validate models/analyses on the Databricks platform supporting prioritized use cases.

Partner with the AI Architect and AI Engineers to ensure data science outputs integrate cleanly into agentic workflows.

Evaluate data quality and feature readiness for AI/ML consumption.

Present findings and recommendations to client stakeholders as needed.

Required Skills:

Strong data science background with hands-on Databricks/Spark ML experience.

Proficiency in Python, statistical modeling, and ML frameworks.

Ability to translate business use cases into data science approaches.

Role Summary:

Partners with the Data Engineering and Agentic AI streams to develop and validate the data science/ML components feeding into the client's AI use cases.

Key Responsibilities:

Develop and validate models/analyses on the Databricks platform supporting prioritized use cases.

Partner with the AI Architect and AI Engineers to ensure data science outputs integrate cleanly into agentic workflows.

Evaluate data quality and feature readiness for AI/ML consumption.

Present findings and recommendations to client stakeholders as needed.

Required Skills:

Strong data science background with hands-on Databricks/Spark ML experience.

Proficiency in Python, statistical modeling, and ML frameworks.

Ability to translate business use cases into data science approaches.