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You will deploy and deliver technical solutions at the intersection of computational chemistry and machine learning, supporting research directions in molecular design across broader gRED and Roche.
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Innovation: Focus on the development and application of new methodologies, technologies, and techniques to gather data and insights including machine learning AI, mobile optimization, gamification, behavioral economics, and virtual/augmented reality.
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Master’s degree in data science, business, sciences statistics, data mining, applied mathematics, business analytics, engineering, computer science or related field (MBA Preferred.
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Educational Requirements:Bachelor's Degree in computer science, machine learning, applied mathematics, econometrics, statistics, engineering, physics, or related discipline required.
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Bachelor’s Degree required from an accredited, not for profit university or college, with degree preferably in Computer Science, Data Science, Statistics, Machine Learning, or Mathematics.
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As a Capital One Machine Learning Engineer (MLE), you'll be part of an Agile team dedicated to productionizing machine learning applications and systems at scale. You’ll focus on machine learning architectural design, develop and review model and application code, and ensure high availability and performance of our machine learning applications.
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Experience in the usage of machine learning/AI tools in life science area(s) and handling life science datasets is preferred. Adapt data science algorithms (supervised and unsupervised learning, decision trees, neural networks, AI based image processing and feature extraction, Bayesian learning, etc) for modeling clinical trial data to support drug development.
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Requirements: Requires a Master’s in Statistics, Computer Science, Data Science, Machine Learning, Applied Math, Operations Research, Economics, or a related field plus two (2) years of experience as a Data Scientist, Data Engineer, or other occupation/position/job title involving research and data analysis.
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Strong understanding of data science approaches including machine and deep learning; background in immunology, biology or oncology a plus. Demonstrated experience applying machine learning and deep learning algorithms to medical imaging analytics including CT, MR, and/or PET scans; experience in additional imaging domain (H&Es, IHC) a plus.
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Strong experience with Python and deep learning frameworks (PyTorch, TensorFlow, Jax, Warp) and relevant specialized deep learning libraries (e.g. PyG, DGL, e3nn) As an Applied Deep Learning Scientist, Geometric Deep Learning, you will join a research and development team passionate about infrastructure development and collaborations with industry and academic partners.
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Experience with/in any of the following is preferred: Applied ML or other data science projects (e.g., Feature Engineering, Predictive Analytics, ML Pipelines); Business Intelligence (BI Tools) including Tableau, Spotfire, Qlikview, and/or Power BI; Cloud based systems on Azure or AWS.
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Strong experience with machine learning, particularly in the context of behavior prediction for autonomous driving. Experience with deep learning frameworks such as TensorFlow or PyTorch.
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Machine learning libraries: TensorFlow, PyTorch, Scikit-learn , Deploying and optimizing different pipelines that support various data science processes. We are looking for candidate who can work without any sponsorship #W2 Machine Learning Engineer with 7+ years of experience in designing, building, and maintaining machine learning models and pipelines.
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Working alongside our generalist consultants, Bain's Advanced Analytics Group (AAG) helps clients across industries solve their biggest problems using our expertise in data science, customer insights, statistics, machine learning, data management, supply chain analytics and data engineering.
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The Department of Mathematical Sciences offers undergraduate degrees in Actuarial Science, Data Science, Mathematical Sciences, and Mathematics & Computer Science, and offers graduate degrees in Pure Mathematics, Mathematics Education, Mathematics for Teaching, Applied Mathematics, and Applied Statistics.
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