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Demonstrated expertise in machine learning, deep learning, and NLP, with a strong portfolio in large language models like GPT-3, BERT, and RAG.Advanced proficiency in Python, with hands-on experience in ML libraries and frameworks such as TensorFlow and PyTorch.
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The Tansey lab focuses on developing novel data science and machine learning methods that address pressing needs in cancer. Demonstrated track research record in the fields of statistics, machine learning, computational biology, data science or related field (computer science, physics, biostatistics, bioinformatics.
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Experience leveraging complex data to drive business decisions, hands-on experience in data science methodologies (predictive analytics, machine learning, patient level data triggers) using R, Python, Databricks, and deep knowledge of Qlik, PowerBI, Tableau for visualization.
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Consult on big data architecture, implement proof of concepts for strategic customer projects, data science and machine learning projects, and validate integrations with cloud services and other 3rd party applications.
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To build a world-class research program for AI- and data-driven innovation in human health, the AI Department is seeking five (5) outstanding candidates for early-career faculty positions at Instructor level with an excellent track record in artificial intelligence, and machine learning to join its faculty as a Eric and Wendy Schmidt AI in Human Health Fellow in 2024.
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New York City (Hybrid On-Site): $181,000 - $181,000 for Data Science PhD InternSan Francisco, California (Hybrid On-site): $191,000 - $191,000 for Data Science PhD Intern. On any given day, you might:Evaluate open-source and internally-developed modeling and analytics tools using real business dataIntegrate internal data with external data sources and APIs to discover and implement actionable insightsDesign and craft data science models to communicate solutions to customers and company leadershipWe'd love to find someone who is.
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MLOps and Deployment: Hands-on experience in deploying machine learning models using CI/CD pipelines, containers (Docker, Kubernetes), and APIs for serving LLM/NLP models at scale. The role:SoFi’s Senior AI Specialist - GenAI, NLP (Banking/Financial Services) is a critical hands-on engineer position in SoFi’s growing independent risk organization focussed on applying data processing/reporting and practical artificial intelligence techniques to solve real world problems.
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We are looking for a passionate Software Engineer, Big-Data Engineer, Machine-Learning Engineer, Full-stack Engineer, or UI/UX Engineer, who can contribute and make a difference in any of the different components of our Knowledge Graph Platform.
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Experience leading teams in data analytics and data visualization, including selecting and applying the appropriate analytical techniques for statistical analysis, predictive modeling, simulation, and machine learning.
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The Data & Analytics department in Legal and Compliance is responsible for designing and optimizing surveillance models, approaches and tools using advanced analytical techniques like supervised and unsupervised machine learning, Natural Language Processing (NLP) and evolving techniques like reinforcement and deep learning as well as graph analytics.
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Hands on experience in dealing with structured and unstructured data from multiple sources and applying machine learning techniques and algorithms to discover patterns, intents and build knowledge repositories and graphs to solve enterprise problems.
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Vertex AI Integration:o Work closely with data scientists and AI specialists to integrate and optimize solutions using Vertex AI on GCP.o Implement and manage machine learning pipelines and models within the Vertex AI environment.
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A Ph. D. in Applied Mathematics, Machine Learning, Computational Biology or Computer Science with emphasis on applications in molecular biology and/or computational pathology (required.
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The ideal candidate will have a strong background in SQL Server development, hands-on experience with Azure Pipeline, and proficiency in working with Databricks for data processing and analytics.
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Audible Content Data Science team partners with business, technology, and product leaders to solve problems related to Content Understanding, Content Evaluation, and Recommendations, relying on Natural Language Processing (NLP), ML, Deep Learning, LLMs/GenAI. We operate in an agile environment in which we own the life cycle of research, design, model development and, often, deployment.
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