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Data Warehousing (e.g., AWS Redshift, Google BigQuery) Apache Hadoop or Apache Spark (for big data processing) AWS, Google Cloud, or Azure (cloud platforms for scalable computing.
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Data Science & Machine Learning Frameworks. Natural Language Processing (NLP) Tools and Frameworks (e.g., Hugging Face, AWS Comprehend Medical for extracting insights from clinical text data.
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Docker, Kubernetes, ECS, EKS or AWS Fargate (for containerization and orchestration of data applications and reproducibility) Data Visualization Tools (e.g., Tableau, Power BI, Plotly) AWS SageMaker (for end-to-end machine learning development, training and scalable machine learning in a managed cloud enviornment.
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Bachelors Degree computer science, artificial intelligence, informatics or closely related field. The Data Scientist will be responsible for driving insights from the vast amounts of patient and environmental data available within our data warehouse.
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HIPAA Compliance (handling sensitive patient data securely) Data Privacy (understanding of privacy laws such as HIPAA, GDPR) Work closely with researcher teams to design analysis specifications, including input data specifications, data cleaning, algorithms, and interpretation of results.
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Data Scientist (Healthcare) 100% Remote. Python (for preprocessing, data analysis, machine learning, scripting) Data Governance & Security. Cloud Computing & DevOps Skills.
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Develop and implement algorithms on existing data warehouse records and identify new external data sources to be ingested to the data warehouse to strengthen analyses. Research and implement AI algorithms, apply off-the-shelf AI and data-centric tools, and collect, store, and maintain data.
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Auditing & Compliance Tools (for ensuring secure and compliant data handling) SAS (common in healthcare data analysis) Data Anonymization or De-identification techniques (for research and compliance.
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XGBoost or LightGBM (for gradient boosting in structured data) Data Tools & Platforms. Clinical Terminologies (e.g., SNOMED, LOINC) Natural Language Processing (NLP) (for analyzing clinical notes or electronic health records.
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TensorFlow, PyTorch, Keras (for deep learning and complex machine learning, including neural networks and advanced AI) ETL Tools (e.g., Informatica, Talend, AWS Glue) AWS Bedrock (for accessing pre-trained LLMs and foundation models without managing infrastructure.
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Experience with Epic or Cerner (popular EHR systems in healthcare) SQL-based Databases (e.g., PostgreSQL, MySQL, Microsoft SQL Server) ICD-10 Coding (for medical diagnosis and procedure classification.
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HL7 (Health Level Seven International standards for electronic health information exchange) Electronic Health Records (EHR) Systems. Predictive Modeling (for patient outcomes, risk analysis) NoSQL Databases (e.g., MongoDB, Cassandra.
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The successful candidate will have demonstrated competence in developing highly scalable artificial intelligence systems with multiple dependencies across teams. R (for statistical computing and bioinformatics.
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CI/CD Pipelines (for automating deployment and monitoring of machine learning models) MATLAB (for algorithm development, though less common in healthcare) Experience with machine learning and statistical analyses are needed.
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AWS Lambda and Step Functions (serverless computing and workflow automation) Dimensionality Reduction (e.g., Principal Component Analysis (PCA), A/B Testing (for clinical trials or health interventions.
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