Data Scientist II - Remote

Overview

We’re seeking a Machine Learning Data Scientist with deep expertise in healthcare claims data to design, build, and deploy advanced analytics and machine learning modeling solutions. In this role, you’ll transform complex administrative and clinical datasets into actionable insights that improve cost efficiency, care quality, and operational performance across the healthcare ecosystem.

You’ll collaborate with data engineers, clinicians, and product teams to develop predictive models, optimize workflows, and support strategic decision-making. This position is ideal for someone who thrives at the intersection of data science, healthcare operations, and modern machine learning.

In Short

• Develop, train, and deploy ML models for healthcare use cases.
• Analyze claims data to predict costs and detect fraud.
• Collaborate with cross-functional teams to define problems.
• Build scalable data pipelines for ML workflows.
• Communicate findings in clear, actionable terms.
• Utilize Python and ML libraries effectively.
• Apply advanced analytics to healthcare data.
• Translate healthcare domain knowledge into analytical solutions.
• Implement modern ML techniques like deep learning and NLP.
• Work with SQL for data manipulation and analysis.

Requirements

• Strong proficiency in Python and relevant ML libraries.
• Hands-on experience with healthcare claims datasets.
• Solid understanding of statistical modeling and ML algorithms.
• Expertise in SQL for data analysis.
• Ability to solve complex problems independently.
• Experience with NLP in healthcare contexts is a plus.
• Familiarity with actuarial concepts and risk scoring.
• Knowledge of deploying models into production environments.
• Background in health economics or biostatistics is beneficial.
• Prior work with FHIR or HL7 standards is advantageous.

Benefits

• Opportunity to make a meaningful impact on healthcare costs.
• Work in a collaborative and innovative environment.
• Flexible working arrangements with remote capabilities.
• Engage with cutting-edge machine learning technologies.
• Contribute to solutions that combat fraud and waste in healthcare.