Clinical Data Scientist (NLP)?

Internal Position Title -
Data Architect/ Data Modeler/ Database Analyst/ Data Warehouse Analyst/ Product Developer

Scope of work
NLP Pipeline Development and Text Analysis - 35%
Build and maintain pipelines using tools such as MedSpaCy, cTAKES, or similar to extract structured variables from clinical notes.
Tune entity recognition, concept mapping, and negation detection to support patient-level feature generation.
Document pipeline logic and validation metrics.

Structured EHR Feature Engineering - 25%
Develop tools to extract, clean, and organize structured EHR variables (e.g., labs, medications, diagnoses).
Apply clinical standards (e.g., OMOP, FHIR) to support semantic consistency and cross-site interoperability.
Transform EHR data into research-ready formats aligned with modeling needs.

Temporal Alignment and Multimodal Integration - 20%
Align clinical events with imaging and biopsy timelines to enable time-resolved analysis.
Support the construction of longitudinal patient records for AI model training and validation.
Troubleshoot data conflicts, gaps, and synchronization issues.

Collaboration and Data Stewardship - 15%
Liaise with institutional data providers to ensure accurate, secure data transfers.
Contribute to protocol development and maintain clear analytic traceability.
Communicate updates and collaborate across project teams and external sites.

Mentorship and Innovation Support - 5%
Provide informal guidance to student researchers or junior analysts.
Recommend new tools or analytic methods to improve pipeline performance.

Level: Intermediate

Skills
Master's degree in bioinformatics, biomedical engineering, computer science, data science, or a related field and three years of relevant experience; or a doctoral degree and one year of experience.
Experience with NLP in the clinical domain using libraries like MedSpaCy or cTAKES
Knowledge of EHR data structures, standards, and interoperability frameworks (e.g., OMOP, FHIR)
Familiarity with Python and clinical data integration tools
Strong organizational skills and attention to reproducibility and versioningExperience collaborating with clinical, data science, or research stakeholders