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We are looking for a passionate, talented, and resourceful Senior Applied Scientist in the field of LLM, Artificial Intelligence (AI), Natural Language Processing (NLP), Recommender Systems and/or Information Retrieval, to invent and build scalable solutions for a state-of-the-art context-aware conversational AI. A successful candidate will have a strong machine learning background and a desire to push the envelope in one or more of the above areas.
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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) 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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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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Data Science & Machine Learning Frameworks. CI/CD Pipelines (for automating deployment and monitoring of machine learning models) Python (for preprocessing, data analysis, machine learning, scripting.
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Experience with machine learning and statistical analyses are needed. Scikit-Learn (for classical machine learning) Apache Hadoop or Apache Spark (for big data processing.
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Large Language Models (LLMs) (for text generation, summarization, etc.) Clinical Terminologies (e.g., SNOMED, LOINC) Data Warehousing (e.g., AWS Redshift, Google BigQuery) Docker, Kubernetes, ECS, EKS or AWS Fargate (for containerization and orchestration of data applications and reproducibility.
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Data Visualization Tools (e.g., Tableau, Power BI, Plotly) ETL Tools (e.g., Informatica, Talend, AWS Glue) Bachelors Degree computer science, artificial intelligence, informatics or closely related field.
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AWS Bedrock (for accessing pre-trained LLMs and foundation models without managing infrastructure) Experience with Epic or Cerner (popular EHR systems in healthcare) AWS, Google Cloud, or Azure (cloud platforms for scalable computing.
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SQL-based Databases (e.g., PostgreSQL, MySQL, Microsoft SQL Server) 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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ICD-10 Coding (for medical diagnosis and procedure classification) HL7 (Health Level Seven International standards for electronic health information exchange) Electronic Health Records (EHR) Systems.
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HIPAA Compliance (handling sensitive patient data securely) Predictive Modeling (for patient outcomes, risk analysis) NoSQL Databases (e.g., MongoDB, Cassandra) Data Privacy (understanding of privacy laws such as HIPAA, GDPR.
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Work closely with researcher teams to design analysis specifications, including input data specifications, data cleaning, algorithms, and interpretation of results. The successful candidate will have demonstrated competence in developing highly scalable artificial intelligence systems with multiple dependencies across teams.
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Data Scientist (Healthcare) 100% Remote. R (for statistical computing and bioinformatics) MATLAB (for algorithm development, though less common in healthcare) Data Governance & Security. AWS Lambda and Step Functions (serverless computing and workflow automation.
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Dimensionality Reduction (e.g., Principal Component Analysis (PCA), Cloud Computing & DevOps Skills. 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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machine learning natural language processing engineer applied mathematics jobs Company: Careerbuilder
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