Data Scientist

Job Summary

The Data Scientist design, build, and operate robust, secure data pipelines that power clinical-research products, analytics dashboards, and downstream data-science workloads. Partner closely with clinicians, investigators, Office of Information Technology and collaborators’ Business Intelligence teams, and external research collaborators to translate complex biomedical data into actionable insights.

Minimum Qualifications

• Bachelor’s degree in Computer Science, Engineering or related field (or equivalent experience).
• Seven (7) years of professional experience in data engineering, software development, or an equivalent mix of education and relevant experience in similar role.
  
  

Preferred Qualifications

• Experience with Snowflake, Microsoft Azure Synapse, or other modern data- warehouse platforms.
• Exposure to machine-learning pipelines (e.g., using OpenAI or other LLM services).
• Experience building/maintaining cloud data platforms (such as GCP , OCI , Linode, AWS , Azure) and data-lake/warehouse solutions, as well as production workload management.
• Hands-on Linux system administration (containerization, networking, security).
  
  

Essential Duties And Responsibilities

• Architect end-to-end pipelines that ingest high-volume de-identified clinical, genomic and phenotypic datasets from collaborators’ EHR systems (Epic Clarity/Caboodle) and cloud storage.
• Build and host production-grade web portals and REST APIs for secure researcher/clinician access supporting role-based permissions and audit trails.
• Leverage OpenAI LLMs (or similar NLP services) to auto-extract Human Phenotype Ontology ( HPO ) terms from de-identified clinical documentation.
• Design high-throughput ETL workflows that parse heterogeneous datasets for ingestion into relational databases and cloud-native warehouses, feeding results into downstream analytics pipelines.
• Design and develop real-time capable analytical systems to integrate with and/or augment EHR systems.
• Perform systems administration for data-platform hosts, including system hardening, patch management, firewall configuration.
• Implement monitoring stacks and custom health checks to maintain near-continuous system availability.
• Translate clinical research requirements into technical specifications, producing clear data-model diagrams, lineage documentation, and data-dictionary artifacts.
• Deliver data-product demos to investigators, effectively showcasing how pipeline outputs support precision medicine reporting.
• Champion standards for metadata management, schema versioning, and test-driven data engineering.
• Other duties as assigned.