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Data Scientist

2 months ago

Pittsburgh, United States Insight Global Healthcare Full time
Job DescriptionJob Description

Data Scientist - Pittsburgh, PA - Hybrid 2x/month on-site

*6 month contract + possible extensions/conversion*

Requirements:

  • Ph.D. in data science, statistics, or computer science OR equivalent
  • Background in Healthcare/familiar with healthcare data
  • 5+ years of experience in NLP model development, validation, extraction, maintain and deployment & AWS
  • Managerial Experience (this role has opportunity to become a lead in future of team)

Job Description:

The Data Scientist will play a vital role in providing insights for the cohort identification and manage the use of two vendor solutions, John Snow Labs and QuantPi. They will develop and implement advanced analytics techniques, and predictive modeling methodologies as part of the DPaaS initiative.

  • Text Data Collection and Preprocessing: Gathering, cleaning, and preprocessing text data from unstructured notes as part of DPaaS. This includes tasks such as tokenization, stemming, and handling text-specific challenges like dealing with different languages, slang, and abbreviations.
  • Model Development and Training: Designing, building, and training NLP models for de-identification of EMR data using techniques such as text classification, sentiment analysis, named entity recognition, machine translation, and language generation. This involves selecting appropriate algorithms, feature engineering, and fine-tuning model parameters.
  • Data Analysis and Insights: Analyzing data to extract meaningful insights and patterns that can inform business decisions. This includes performing statistical analysis, visualizing data, and communicating findings to stakeholders. Staying updated with the latest research and integrating state-of-the-art methods into projects.
  • Evaluation and Optimization: Evaluating NLP models using appropriate metrics (e.g., precision, recall, F1 score) and techniques (e.g., cross-validation, confusion matrix). Continuously optimizing models to improve accuracy, efficiency, and scalability.