Senior Data Scientist - Healthcare AI/ML
P
Prominence AdvisorsHealthcare
United StatesFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 3–5+ years
- Required Skills
- AWSPythonSQLGCPMachine LearningNumpySnowflakeAzurePandasDatabricksscikit-learnPrompt EngineeringMLOpsGenerative AI
Requirements
- 3–5+ years of professional experience in data science, machine learning, or a closely related quantitative role
- Strong proficiency in Python for data science (pandas, NumPy, scikit-learn)
- Strong proficiency in SQL for data extraction and manipulation
- Hands-on experience building, validating, and deploying machine learning models (classification, regression, clustering, time-series forecasting)
- Experience with MLOps best practices (model versioning, CI/CD for ML, experiment tracking with MLflow or similar, continuous monitoring)
- Practical experience building AI-powered solutions using LLM APIs, prompt engineering, and RAG pipelines
- Experience with cloud-based data and ML platforms, including at least one of the following: AWS (SageMaker, Redshift, S3), Azure (Azure ML, Synapse, Data Factory), GCP (Vertex AI, BigQuery), Databricks, or Snowflake
- Strong communication skills with the ability to present technical findings to non-technical audiences
- Advanced degree (Master's or PhD) in a quantitative field such as computer science, statistics, biostatistics, or a related discipline (Desired)
- Healthcare industry knowledge and experience including EHR data, claims, HL7/FHIR, clinical terminologies (Desired)
- Experience with population health analytics, risk stratification, or social determinants of health (Desired)
- Understanding of research enablement workflows including cohort identification, clinical trial matching, and outcomes research (Desired)
- Experience with vector databases, embeddings, fine-tuning, or LLM evaluation frameworks (Desired)
- Familiarity with data visualization and BI tools (Tableau, Power BI, or Looker) (Desired)
- Revenue cycle, staffing optimization, or other operational analytics experience (Desired)
Responsibilities
- Design, build, validate, and deploy machine learning models across the full data science lifecycle, including exploratory analysis, feature engineering, model development, validation, deployment, and monitoring
- Develop advanced analytics solutions for clinical prediction, population health, operational optimization, and research enablement
- Build AI-powered applications leveraging LLM APIs, prompt engineering, and RAG pipelines to support clinical and operational workflows
- Ability to work with diverse healthcare data sources including EHRs, claims, and clinical registries
- Translate complex analytical findings into meaningful, actionable insights for clinical and operational stakeholders
- Manage documentation of best practices and scalable frameworks that can be applied across client engagements
- Mentor and guide client counterparts to build the skills needed to sustain and expand on project deliverables
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