Horizontal Talent

Sr Data Scientist

Eden Prairie, Minnesota, USA · Senior · full-time

Sponsorship not specified$22k-$45kDetected 42 days ago

PythonSQLMachine LearningDeep LearningPyTorchData ScienceAgentic AIStatisticsResearchProblem Solving

About the role

This role offers the opportunity to work on meaningful projects that enhance efficiency and promote cost savings.
Conduct robust statistical analyses to inform critical business decisions and enhance operational outcomes.
Curate high-quality datasets in partnership with data stewards and engineering teams.

Collaborate with cross-functional teams to translate business challenges into actionable analytical objectives.
Document processes and results to ensure transparency and reproducibility across systems.

Skills Advanced degree in a technical field such as Applied Mathematics, Computer Science, or Statistics.
Experience with Agentic AI frameworks and their application in real-world scenarios.

The pay range for this role is $22 - $45 per hour based on qualifications and experience.

Join a dynamic team as a Senior Data Scientist, where you'll leverage your expertise in AI and machine learning to drive impactful solutions in the healthcare sector.
Design and implement advanced models utilizing statistical techniques and machine learning algorithms.
Proficiency in machine learning and statistical modeling techniques.
Preferred Skills Familiarity with US healthcare data, including claims and clinical records.
Knowledge of deep learning frameworks such as PyTorch or TensorFlow.
Ability to understand and evaluate emerging research in AI and machine learning.
We value diversity, equity, and inclusion and strive to create an environment where all individuals feel empowered and respected.
For those that join the team, we offer competitive compensation and benefits including medical, dental, vision, and retirement.

Check out all we have to offer and how you can become part of the Horizontal Talent Team.

This listing is sourced directly from Horizontal Talent's careers page and normalized into a canonical job model.