Senior Data Scientist

Accountabilities

• Develop, train, validate, and deploy machine learning models to support healthcare-focused products, including applications in medication intelligence, risk monitoring, and operational optimization.
• Translate ambiguous business problems into well-defined analytical questions, hypotheses, and data science solutions.
• Design and implement end-to-end data science workflows, including feature engineering, model training, evaluation, and productionization via APIs.
• Define, track, and monitor KPIs to evaluate model performance, business impact, and long-term value delivery.
• Collaborate closely with product managers and stakeholders to scope opportunities, build prototypes, and refine solution requirements.
• Present insights, model outcomes, and recommendations to both technical and non-technical audiences, including customers and executives.
• Partner with engineering teams to deploy scalable ML solutions using cloud infrastructure and production-grade systems.
• Continuously analyze healthcare datasets and adapt models as new data sources, regulations, and business needs evolve.
• Contribute to improving team practices through code reviews, documentation, and knowledge sharing across the data organization.
• Support roadmap planning by assessing technical feasibility, risks, and opportunities for new data science initiatives.
• Promote best practices in experimentation, statistical rigor, and reproducible research across the team.

Requirements

• Bachelor’s degree in Data Science, Statistics, Computer Science, Mathematics, or a related quantitative field; Master’s or PhD preferred.
• 5+ years of professional experience in data science, machine learning, or applied analytics roles.
• Strong programming skills in Python with experience building and deploying production-ready ML models.
• Experience developing APIs for model serving using frameworks such as Flask or FastAPI.
• Strong understanding of statistical methods, probability distributions, hypothesis testing, and data transformations.
• Hands-on experience with supervised and unsupervised learning techniques, including tree-based models, clustering, and regression methods.
• Experience working with AWS cloud services such as SageMaker, Athena, RDS, Glue, and related tooling.
• Strong SQL skills with the ability to query, transform, and analyze large datasets efficiently.
• Experience with version control systems such as Git and collaborative development workflows.
• Ability to communicate complex technical concepts clearly to both technical and non-technical audiences.
• Customer-focused mindset with experience presenting insights and recommendations to external stakeholders.
• Familiarity with modern ML tooling and willingness to leverage AI-assisted development tools to accelerate experimentation.
• Experience in healthcare, pharmaceuticals, or regulated data environments is a strong plus.
• Exposure to MLOps practices, containerization, or CI/CD pipelines is considered advantageous.

Benefits

• Competitive salary with performance-based compensation opportunities.
• Fully remote work environment available across the United States.
• Comprehensive medical, dental, and vision insurance coverage.
• 401(k) retirement savings plan.
• Flexible and generous paid time off policy.
• Opportunity to work on high-impact healthcare AI and machine learning applications.
• Access to modern cloud infrastructure and advanced data science tooling.
• Collaborative, innovation-driven environment with strong technical mentorship.
• Professional growth opportunities in a fast-scaling healthcare technology organization.

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