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Senior Data Scientist (Biomedical NLP) | Mission-Driven MedTech Stealth Start-up

  • Location

    San Francisco

  • Sector:

    Data Science

  • Job type:

    Permanent

  • Contact:

    Amelia Jones

  • Contact email:

    amelia@intelletec.com

  • Startdate:

    ASAP

  • Consultant:

    Amelia Jones

Senior Data Scientist (Biomedical NLP) | Mission-Driven MedTech Stealth Start-up

 

Intelletec has partnered with a Mission-Driven MedTech company, who's products will have a real-life impact on the future of diagnosing and treating illnesses, injuries, and diseases. The mission is to improve the quality and efficiency of healthcare; making the highest quality healthcare available to everyone. 

 

 

Senior NLP data scientists define and implement strategies to handle free-form medical text, including developing internal NLP packages and/or heavily customizing open-source solutions.  They contribute to the development of ontologies and associated biomedical tooling, and support various other data science initiatives.

 

Responsibilities

  • Design and implement NLP pipelines to extract structured content from free-form radiology text

  • Help define and build core ontologies

  • Define NLP methodologies, including information models and ontologies

  • Develop or heavily customize open source solutions for Named Entity Recognition, POS tagging, and syntactic dependency parsing that will work on domain-specific medical data

  • Develop tools for manual and automated label annotation of free-text radiology reports

  • Work with annotation team to develop labeled training and test datasets

  • Build internal tools and define associated statistics to provide insight into algorithm performance

 

Requirements

  • Advanced degree in computational linguistics, informatics, or related field 

  • Experience building and using ontologies and knowledge graphs to support natural language processing, data integration, and analytics

  • Deep understanding of core NLP/NLU algorithms, tasks, model architectures, and open-source resources: CRFs, Neural Networks (LSTMs, dense vector embeddings), rules-based/regex approaches. Named Entity Recognition and Disambiguation, POS tagging, syntactic dependency parsing. spaCy, gensim, NLTK, StanfordNLP, annotation tools (e.g. Brat, Daccano)

  • Track record building and productionizing NLP models in an industry setting

  • Expert coding ability

  • Experience leading teams and mentoring junior data scientists

Benefits

  • Stock

  • Competitive salaries

  • Paid time off

  • Medical insurance

  • 401(k)

  • Apple equipment

  • Dedicated research computing resources

  • Catered lunches and team events

  • Sponsorship for conferences