Product-Focused Data Scientist (Analytics & MLOps)

Cardinal Health; San Juan (PR); On-site; USD 94,900 - 122,040; Full time

Benefits offered by this job

Medical, dental and vision coverage

Paid time off plan

Health savings account (HSA)

401k savings plan

MyFlexPay access to wages before pay">

Job summary

Cardinal Health is seeking a data science professional who partners with engineering to turn analytical concepts into real, working business solutions. You will be hands-on with data, building prototypes and outputs, while focusing on ensuring our products serve clear business value and are integrated into daily workflows.

The role emphasizes a product adoption mindset, measurable business impact, and collaboration with cross-functional teams to deploy models in production environments.

Qualifications

• 4-8 years of experience preferred.
• Bachelor's degree in related field or equivalent work experience preferred.
• Strong proficiency in SQL and Python.
• Experience with Agile and SDLC frameworks.
• Experience with Git and CI/CD tools (GitHub Actions, GitLab, Azure DevOps, Jenkins).
• Experience containerizing applications and ML models with Docker.
• Experience deploying or supporting workloads on Kubernetes.

Responsibilities

• Hands-on with data, building prototypes and outputs.
• Design outputs and tracking metrics for business impact.
• Bridge between technical development and financial workflows.
• Ensure models are trusted, usable and integrated in daily processes.

Skills

SQL

Python

CI/CD

Git

Docker

Kubernetes

Agile

SDLC

Education

Bachelor's degree

Tools

GitHub Actions

GitLab

Azure DevOps

Jenkins

Job description

Cardinal Health is seeking a data science professional who partners with engineering to turn analytical concepts into real, working business solutions. You will be hands-on with data, building prototypes and outputs, while focusing on ensuring our products serve clear business value and are integrated into daily workflows.

The role emphasizes a product adoption mindset, measurable business impact, and collaboration with cross-functional teams to deploy models in production environments.