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The Principal ML Research Scientist will also have a hands-on roll and is expected to customize and create various machine learning algorithms to operate over multi-domain data and optimizing the performance of those algorithms on the data.
$122,200 - $220,900 a yearExpandApply NowActive JobUpdated Today - UpvoteDownvoteShare Job
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The successful applicant will design and operate a framework for Machine Learning Operations (MLOps), advise on software engineering for ML, and ensure consistency with cloud architectural principles.
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Proven working Experience on GCP BQ, SQL, Hadoop, and machine learning. Ability to perform hands-on development as an when required. All applicants will be evaluated solely on the basis of their ability, competence and their proven capability to perform the functions outlined in the corresponding role.
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Hands-on experience in working with frameworks like Tensorflow, PyTorch, SkLearn. 7+ years of experience with 5+ in Machine Learning/Data Science. Hands-on experience in Building, training, and deploying ML models along with strong Marketing domain and Use cases experience.
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Hands-on experience using relational database management systems (Teradata) and Cloud computing platforms (AWS, Databricks) Assist in the development of new Machine Learning models to predict cost trends and customer behaviors and perform data mining by applying machine learning and supervised learning algorithms.
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The ideal candidate will have hands-on experience in LLM frameworks, architecture optimisation, approximate inference, evaluation of LLMs, model distillation/compression, adaptive computation, hardware-aware modelling, and/or AI hardware/accelerator architecture, as well as the motivation and ambition to apply this in a real-world setting.
$112,000 - $238,600 a yearFull-timeExpandApply NowActive JobUpdated Today - UpvoteDownvoteShare Job
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As a Data Scientist at Capital One, youll be part of a team thats 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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Proven experience in developing and deploying production ML models using analytical methods such as regression, decision trees, support vector machines, supervised and unsupervised learning. Proficiency in Python, R, Spark and SQL for data analysis, modeling, and manipulation.
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You have hands-on experience developing data science solutions using open-source tools and cloud computing platforms. Business Card & Payments Data Science team builds industry leading machine learning models to empower credit underwriting decisionings, supports advancement in Capital One business card product strategies, decisioning and credit infrastructures.
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Hands-on experience with building Deep Learning applications. The Applied Machine Learning Data Science team is an outstanding combination of machine learning techniques, algorithms, as well as real world business solutions that will impact billions of devices in the world.
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Build machine learning models through all phases of development, from design through training, evaluation, validation, and implementation. At least 3 years’ experience with machine learning.
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3+ years in an analytics role with hands-on experience developing predictive models using machine learning techniques and conducting statistical analyses. Able to independently build enterprise grade Machine Learning models.
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Good understanding of Machine Learning Algorithms & Deep Learning Algorithms. Hands on CI/CD Experience with Real time computer vision using DNN, CNN, RNN, LSTM. Our client is looking Machine Learning Lead / Architect for Full Time project in Austin, TX (Onsite) below is the detailed requirements.
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We're looking for exceptionally skilled and creative scientists and engineers eager to get involved in hands-on work to improve our speech technologies by applying machine learning.
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Senior Associate Data Scientist - Machine Learning, Financial Services. Our team has a relentless focus on the craft of modeling and innovation, 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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