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A Masters’ degree in computer science, AI/ML, data science, mathematics, statistics, applied linguistics/psychology/neuroscience and related fields, or 2+ years of equivalent experience.
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The Human-centered AI team in Digital Health Oncology R&D is looking for an Associate Director of Data Science to specialize in the development of novel digital endpoints using data science, statistical learning, and innovative machine learning methods on multi-modal datasets including clinical data, real world evidence, imaging, wearables, and other biomedical data sources to address patient burden.
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Apply data science expertise in machine learning, deep learning, statistics, text-mining/NLP, forecasting, and optimization to multiple analytics projects. Expertise with core data science languages (predominantly with Python, and nice to have R & Scala), experience working with Snowflake and other different database systems (such as SQL, NoSQL.
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Build and lead the data science team translating small molecule and proteomic drug discovery challenges into breakthroughs via deep learning applied to computational chemistry.
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Expertise in at least one ML framework (PyTorch/TensorFlow/Jax) and robust experience in the Python data science ecosystem. Designing, training and fine-tuning deep learning models to engineer proteins, RNA or DNA.
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PhD in Chemistry, Artificial Intelligence, Machine Learning, Data Science, Material Science, or a closely related field. Employ advanced AI and machine learning models, including deep learning and predictive analytics, to identify novel materials and predict their performance within battery systems.
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A PhD degree or Masters in Computer Science, Information and Engineering Sciences, Computational Linguistics, Applied Mathematics or Applied Sciences with a focus on machine learning and deep learning.
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Projects include, but are not limited to, use of real-world data for modeling and prediction of disease severity and progression, discovery of disease subtypes, risk stratification, and precision medicine using traditional biostatistical methodologies and advanced methodologies from machine learning and/or deep learning.
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The Lead Data Science and Machine Learning (ML) directs and executes Ontada's technology roadmaps in ML and Natural Language Processing (NLP) Data Platform roadmap through close collaboration with Ontada commercial, chart abstraction and engineering teams.
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Significant relevant experience after your MD/PhD/postdoc including quantitative expertise in biostatistics, bioinformatics, computer biology, statistical genetics, clinical imaging, machine learning, or data science; interpreting and publishing studies consisting of "omics" data e.g. genetics, genomics, proteomics; closely partnering with/leading data scientists.
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PhD in machine learning, computer science, physics, or related field and 8+ years’ experience with data science, developing and prototyping large-scale machine learning models, such as deep learning, CNN, GNN, and transformer-based foundation models OR MS and 10+ years of experience OR BS and 14+ years of experience.
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2) Establish robust and scalable data management pipeline; (3) Apply Artificial Intelligence in different business domains (Data Science, Machine Learning, Statistical Analysis, etc.
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As a Data Scientist at Capital One, you’ll be part of a team that’s leading the next wave of disruption at a whole new scale, using the latest in computing and machine learning technologies and operating across billions of customer records to unlock the big opportunities that help everyday people save money, time and agony in their financial lives.
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The Adobe Document Cloud Data Science team is looking for Machine Learning Engineer who can apply computer vision techniques and help us build solutions that will drive the success of our Document Cloud business.
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Degree in computer science, data science, software engineering, computational chemistry or other fields with relevant experience. You have experience using AWS services such as S3, EC2, ECS, AWS Batch, and Sagemaker to reduce runtime for training and evaluating deep learning models.
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deep learning data science jobs in Lexington, MA
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