Clinical Data Scientist (Clinical Informatics Team)
Tasks
- Build AI data pipelines for structured and unstructured clinical data
- Build agentic AI and human in the loop validation workflows
- Build internal QA and review applications and dashboards
- Conduct literature mining and biomedical information retrieval
- Create evaluation frameworks for extraction quality and reliability
- Design longitudinal data representations and feature engineering
- Develop ETL and data transformation pipelines
- Develop LLM systems for clinical note extraction
- Develop automated clinical data curation workflows
- Develop reusable structured data assets for analytics and simulation
- Handle missingness temporal relationships and imputation
- Implement phenotype extraction and entity normalization
- Integrate and harmonize heterogeneous clinical datasets
- Optimize scalability robustness and computational efficiency
Perks/Benefits
- N/A
Skills/Tech-stack
APIs | Clinical data | Clinical data curation | Cloud Computing | Computational Efficiency | Cost Optimization | Data Curation | Data Imputation | Data Transformation | Data handling | Data harmonization | Databricks | ETL | Entity Normalization | FastAPI | Feature Engineering | Human-in-the-loop | Information Retrieval | LLM | Language Processing | Latency optimization | MLflow | Machine Learning | Missing data | Missing data handling | Monitoring | Natural Language | Natural Language Processing | Operational cost | Operational cost optimization | Phenotype extraction | PyTorch | Python | Retrieval-Augmented Generation | Robustness | SQL | Scalability | Snowflake | Software Engineering | Spark | Streamlit | Temporal modeling | Testing | The Loop | Validation
Education
Roles
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