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Strong technical and consultative experience managing ESI through the EDRM including staging, processing, culling, email threading, clustering, communication analysis, machine learning, predictive modeling, etc.
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Build and maintain familiarity with Otsuka Machine Learning tech stack including AWS, Kubernetes, Snowflake, and Dataiku. Profound grasp of Machine Learning lifecycle - feature engineering, training, validation, scaling, deployment, scoring, monitoring, and feedback loop.
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PSC advances science across a wide spectrum of fields including artificial intelligence / machine learning, medical imaging, weather modeling, cell biology, and genomics. Master’s or PhD degree required, with preferred education/experience in Biology, Biomedical Informatics, Biomedical Engineering, Computational Biology, Computer Science, Data Science, AI/ML, or related fields.
Full-timeExpandApply NowActive JobUpdated 2 days ago - UpvoteDownvoteShare Job
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To make this vast amount of information more accessible, we have built a variety of tools including a search engine, a machine-learning-driven search intent classifier, an LLM-driven question -answering system and an ontology/ taxonomy all supported by reference data about companies, industries, and our analysts.
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Together, Appen and Figure Eight Federal, combine the best of human and machine intelligence to provide high-quality annotated training data that powers the world’s most innovative machine learning (ML) mission solutions.
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Manage matters within Cardinal Health's internally hosted E-Discovery platform including processing, data culling, email threading, predictive analytics and workflows, tagging layout design and implementation, custom field creation, batch creation, search creation, production sweeps and QC, and production creation and transfer all with an eye toward best practices.
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At least 2 years of experience with predictive modeling and analysis, applying various machine learning techniques including supervised/unsupervised learning, deep learning, and reinforcement learning.
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You have experience in backend engineering, data infrastructure, search infrastructure or natural language processing/machine learning. You'll improve search latency and relevancy using large scale data systems and machine learning.
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The eDiscovery team at Cardinal Health is a specialized team of eDiscovery professionals, with backgrounds in Legal, IT, Project Management, and Data Analytics. Experience consulting on and implementing effective preservation and collection strategies for various data types outside of email, including Forensic Laptop Imaging, Teams, Mobile Messaging, Slack, Salesforce, Structured Data, etc.
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Utilize industry standard electronic discovery and data forensic hardware and software to ensure data integrity and defensibility. Utilize a variety of archiving and data analysis applications and provide technical subject matter expertise to legal professionals.
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By innovating and implementing state-of-the-art machine learning, data science, and large language model solutions, you'll help shape the future of sustainable fashion retail. Design and develop machine learning models to power our personalization and search ranking algorithms.
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A general understanding of data privacy and protection laws would be a plus as is familiarity with artificial intelligence and machine learning issues. We’ve been engaged on an exclusive basis to assist a longstanding and favorite client, a nationally recognized IT services business headquartered in Northern Virginia, in its search to hire an Associate General Counsel (“AGC.
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8 years of eDiscovery Project Management/Consultant experience preferred. Conduct preservation, collections and processing using the following tools: EnCase, Cellebrite, Nuix, Heureka, Relativity One, M365 Compliance Center.
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Break down open-ended search relevance problems into engineering strategies by utilizing machine learning, data analysis, and experimentation design. We are looking for a highly-skilled and experienced machine learning engineer to join the Search team to lead our search quality efforts through their early ML phase (and beyond.
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We are applying world-class machine learning, artificial intelligence, visual search, data storage, and client technologies to one of the largest sports with data sets of ideas in one place.
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