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Deep understanding and experience with advanced analytics, Machine Learning (ML), Deep Learning (DL), Natural Language Processing (NLP) or Computer Vision (at least 3 of these areas.
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Natural Language Processing (NLP) Tools and Frameworks (e.g., Hugging Face, AWS Comprehend Medical for extracting insights from clinical text data) Python (for preprocessing, data analysis, machine learning, scripting.
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We are on the forefront of CBRN defense and we are looking for talented Data Scientists that have applied experience in the fields of artificial intelligence, machine learning and/or natural language processing to join our team.
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Natural Language Processing (NLP) (for analyzing clinical notes or electronic health records) Data Science & Machine Learning Frameworks. Apache Hadoop or Apache Spark (for big data processing.
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TensorFlow, PyTorch, Keras (for deep learning and complex machine learning, including neural networks and advanced AI) AWS SageMaker (for end-to-end machine learning development, training and scalable machine learning in a managed cloud enviornment.
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CI/CD Pipelines (for automating deployment and monitoring of machine learning models) Experience with machine learning and statistical analyses are needed. Scikit-Learn (for classical machine learning.
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Data Warehousing (e.g., AWS Redshift, Google BigQuery) 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.
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The Data Scientist will be responsible for driving insights from the vast amounts of patient and environmental data available within our data warehouse. HIPAA Compliance (handling sensitive patient data securely.
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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. R (for statistical computing and bioinformatics) Data Governance & Security. 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.
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Research and implement AI algorithms, apply off-the-shelf AI and data-centric tools, and collect, store, and maintain data. Auditing & Compliance Tools (for ensuring secure and compliant data handling.
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SAS (common in healthcare data analysis) Data Anonymization or De-identification techniques (for research and compliance) Large Language Models (LLMs) (for text generation, summarization, etc.
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Good understanding of machine learning, deep learning (including LLMs) and natural language processing and ability to optimize machine learning models to adapt to solving various kinds of issues.
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XGBoost or LightGBM (for gradient boosting in structured data) Data Tools & Platforms. Dimensionality Reduction (e.g., Principal Component Analysis (PCA), Clinical Terminologies (e.g., SNOMED, LOINC.
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ETL Tools (e.g., Informatica, Talend, AWS Glue) Bachelors Degree computer science, artificial intelligence, informatics or closely related field. AWS Bedrock (for accessing pre-trained LLMs and foundation models without managing infrastructure.
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machine learning natural language processing r python data engineering jobs Title: senior principal
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