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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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Demonstrated experience with machine learning algorithms (regression, semi-supervised learning, deep learning, reinforcement learning, time series analysis, predictive modeling, data mining, cognitive computing.
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We are seeking a collaborative bioinformatics and machine learning scientist to join the Computational Design and Predictive Modeling team in Cambridge, MA. You will develop novel bioinformatics and machine learning solutions and apply them to next-generation sequencing (NGS) data to discover and characterize biotherapeutics.
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Analytical Skill, Analytical Skill, Analytical Thinking, Chemical Biology, Chemistry, Clinical Trials, Collaboration, Communication Skills, Curiosity, Data, Data Analysis, Design Of Experiments, Detail-Oriented, Drug Development, Drug Discovery, Ethics, Gene Therapy, Immunology, Inventory Management, Laboratory, Lifesciences, Machine Learning (Ml), Management, Medical Research, Molecular Biology {+ 10 more.
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They will work to (1) further integrate CFPS technology into our HTP screening workflows at the intersection of synthetic biology, microfluidics, automation, and machine learning, 2) optimize and expand our CFPS workflows across modalities, and 3) work closely with our production scientists to design and implement productionized workflows.
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Emerging ML is the data science and machine learning team inside Capital One’s Applied Research organization. Build machine learning models through all phases of development, from design through training, evaluation and validation, and partner with engineering teams to operationalize them in scalable and resilient production systems that serve 50+ million customers.
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Analysis scenarios will involve applying cutting-edge bioinformatics, statistics, and machine learning/AI methods; working with clinical and large-scale omics data and biomarker data sets that could include WGS/WES, RNSeq, scRNSeq, IHC, Spatial Transcriptomics, etc; from BMS’s industrial-leading late-stage oncology clinical studies; influencing development strategies and advancing BMS hematology/oncology pipeline.
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Post Doctoral Fellow - AI / Machine Learning Data Scientist. As a Post Doctoral Fellow within TDC you will be responsible for developing, maintaining and deploying machine learning models for biopharmaceutical processes, such as upstream biologics, where having a fundamental understanding of process engineering would be preferred.
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Expertise in image processing and embedding methodologies, neural networks, machine learning, data dimension reduction methodologies is a plus. Preference will be given to candidates experienced in cellular assay development leveraging disease and physiologically relevant models (e.g., primary cells, iPSC-derived cells, 3D cell culture.
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Develop and apply bioinformatics, computational biology, and machine learning tools to generate insights and hypotheses from high-dimensional molecular datasets, with an initial focus on proteomic data.
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Principal Scientist, Protein Analytics & Biophysics-Target Binding Generate:Biomedicines is a new kind of therapeutics company – existing at the intersection of machine learning, biological engineering, and medicine – pioneering Generative Biology to create breakthrough medicines where novel therapeutics are computationally generated, instead of being discovered.
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Python, Java or Perl- Experience in developing deep learning models for speech, music, sound detection applicationsPREFERRED QUALIFICATIONS- 5+ years of building machine learning models or developing algorithms for business application experience- Experience with conducting research in a corporate setting- Well versed in the latest deep learning architecture and training techniques for audio and multimodal foundation model development.
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Be part of making the entire Research & Development cycle digitally-enabled: improving clinical trials and data collection, Digital therapeutics for rapid diagnostic, wearables, machine learning and visual analytics for informed decision-making, integrated data to improve patient outcomes, and insights to inform the next generation of products.
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This position is key for achieving the mission of the Computational Antigen Design: pioneering, testing and implementing innovative cutting-edge biological science and novel Structural Biology/Machine Learning technology solutions and concepts towards antigen design.
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PhD in Computer Science, Machine Learning, Robotics, or a related field. MS in Computer Science, Machine Learning, Robotics, or a related field. Are you passionate about using machine learning to drive robot behavior.
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scientist machine learning jobs in Lexington, MA
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