NLP Analyst – Healthcare & General Text Analytics

Job Summary

We are seeking an experienced NLP Analyst with a strong background in text analytics and hands-on expertise in healthcare-specific NLP. This role is responsible for building and deploying robust NLP pipelines that process both general and clinical text data, driving initiatives from sentiment analysis to clinical decision support. The ideal candidate will help enhance patient care and operational efficiency by developing innovative NLP solutions and ensuring compliance with industry standards.

Responsibilities

• NLP Development & Execution
  • Develop and optimize NLP workflows including tokenization, part-of-speech tagging, lemmatization, entity extraction, and summarization.
  • Implement and evaluate machine learning models such as BERT, GPT, RoBERTa, XGBoost, LSTM, and custom architectures.

• Healthcare-Specific NLP
  • Apply clinical Named Entity Recognition (NER) using established ontologies like SNOMED CT, ICD-10, RxNorm, and UMLS.
  • Develop context-aware extractors to identify negation, temporality, and experiencer attributes in clinical notes.
  • Disambiguate medical abbreviations (e.g., converting “HTN” into Hypertension) and normalize structured clinical data.

• Annotation & Labeling
  • Design and manage clinical annotation schemas.
  • Utilize tools such as Prodigy, Label Studio, or Amazon SageMaker Ground Truth for data labeling.
  • Ensure PHI de-identification and labeling compliance in alignment with HIPAA standards.

• Data & Evaluation
  • Work with diverse text sources including social media, EHR notes, claims data, discharge summaries, and radiology/pathology reports.
  • Apply and interpret evaluation metrics like F1, BLEU, ROUGE, and concept-level recall tailored for clinical NLP.

• Deployment & Integration
  • Deploy NLP models via REST APIs, batch pipelines, or real-time inference setups.
  • Integrate models with EHR systems using HL7 FHIR and/or plugins to support clinical decision support or coding automation.

Qualifications

• Experience & Education
  • 5+ years of experience in NLP, with at least 2 years in healthcare-specific NLP preferred.
  • A degree in Computer Science, Data Science, or a related field (BTech, BSc, MTech, or PhD).

• Technical Proficiencies
  • Advanced proficiency in Python and libraries such as spaCy, NLTK, Scikit-learn, and Transformers (HuggingFace).
  • Experience with healthcare NLP tools like MedSpaCy, ScispaCy, MetaMap, Stanza, cTAKES, and CLAMP.
  • Familiarity with specialized models such as BioBERT, ClinicalBERT, or GatorTron.

• Healthcare Standards
  • Solid understanding of medical terminologies and coding systems including UMLS, LOINC, ICD-10, SNOMED CT, and CPT.
  • Experience working with clinical datasets, such as MIMIC-III/IV or comparable sources.

Preferred Skills

• Experience in developing custom NLP pipelines and troubleshooting complex NLP challenges.
• In-depth knowledge of clinical text annotation processes and data labeling techniques.
• Proven ability to integrate NLP models seamlessly with healthcare IT systems.
• Familiarity with working within HIPAA compliance frameworks and managing sensitive health data.

Experience

• Minimum of 5 years of experience in NLP with proven expertise in healthcare-specific applications for at least 2 years.
• Demonstrated success in deploying production-level NLP solutions in a dynamic environment.

Environment

• Work Setting: Fully remote role offering flexibility and collaboration across distributed teams.
• Collaboration: Engage with cross-functional teams in a dynamic virtual workspace.
• Methodology: Agile work environment with a focus on innovation and continuous improvement.

Tools

• Python, spaCy, NLTK, Scikit-learn, Transformers (HuggingFace)
• MedSpaCy, ScispaCy, MetaMap, Stanza, cTAKES, CLAMP
• Prodigy, Label Studio, Amazon SageMaker Ground Truth