Drive high-impact Clinical Data Science initiatives focused on healthcare outcomes, intervention effectiveness, and AI-driven innovation. Develop advanced analytical solutions that drive measurable business and clinical impact. Partner with clinical, business, product, and technology stakeholders to evaluate healthcare interventions and design robust analytical frameworks.
Key Highlights
Key Responsibilities
Technical Skills Required
Benefits & Perks
Nice to Have
Job Description
Title: Principal Data Scientist
Openings: 1
Duration: 12mo contract + extensions
Hourly Pay: approx. $65-75/hr
Schedule: REMOTE anywhere in the US, Mon-Fri day shift (core hours, EST/CST)
Start Date: ASAP
Interview Times: Schedule within 24hrs, Mon 7/20 - Fri 7/24
Interview Process: 3 rounds
Auth: GC/USC preferred so client can convert to FTE (they cannot sponsor directly), C2C possible for the right fit
Must-Haves
- 7yrs+ of experience in Data Science, Machine Learning, Applied Statistics, or AI solution development
- Experience working within healthcare, Medicare, payer, population health, clinical analytics, or healthcare outcomes research
- Strong expertise in causal inference, intervention analytics, observational studies, treatment-effect estimation, and statistical experimentation
- Proven experience evaluating and mitigating selection bias, confounding variables, data leakage, and model validity concerns
- Experience designing, building, deploying, and scaling machine learning models in production environments
- Strong knowledge of machine learning, predictive analytics, NLP, and modern Generative AI technologies including LLMs and RAG architectures
- Strong programming experience with Python, SQL, Spark, and/or PySpark
- Experience communicating complex analytical findings and model decisions to executive, clinical, and business stakeholders
- Solid MLOps experience including model deployment, monitoring, CI/CD, experiment tracking, and model governance
- Experience working in cloud environments (GCP, AWS, or Azure)
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Plusses
- Hands-on GCP experience including Vertex AI, BigQuery, Cloud Storage, and ML orchestration pipelines
- Experience applying causal inference methodologies in healthcare outcomes, member engagement, population health, quality improvement, or intervention effectiveness programs
- Experience with causal discovery techniques and advanced statistical methodologies
- Background in epidemiology, biostatistics, health economics, clinical research, or healthcare analytics
- Experience working with enterprise-scale healthcare datasets and distributed analytics environments
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Job Description
A large Health Insurer is seeking an exceptionally talented Principal Data Scientist to support high-impact Clinical Data Science initiatives focused on healthcare outcomes, intervention effectiveness, and AI-driven innovation.
This individual will serve as a senior technical leader responsible for developing advanced analytical solutions that drive measurable business and clinical impact. The ideal candidate combines deep statistical expertise with modern machine learning and AI capabilities, bringing a strong foundation in causal inference, experimental design, healthcare analytics, and predictive modeling.
The Principal Data Scientist will partner with clinical, business, product, and technology stakeholders to evaluate healthcare interventions, identify drivers of member outcomes, design robust analytical frameworks, and develop production-grade machine learning solutions. This person must be comfortable assessing treatment effects, addressing potential sources of bias, evaluating model performance, and translating complex analytical findings into actionable recommendations.
In addition to traditional data science expertise, the ideal candidate will have experience leveraging modern AI technologies including Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and agentic AI frameworks to solve complex business and healthcare challenges.
Success in this role requires a unique combination of:
- Advanced statistical reasoning
- Causal inference expertise
- Healthcare domain knowledge
- Machine learning and AI proficiency
- Strong stakeholder communication skills
- The ability to move analytical solutions from research through production deployment
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