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Data science, machine learning, optimization models, PhD in Machine Learning, Computer Science, Information Technology, Operations Research, Statistics, Applied Mathematics, Econometrics, Publications or active peer reviewer in related journals or conference, Successful completion of one or more assessments in Python, Spark, Scala, or R, Using open source frameworks (for example, scikit learn, tensorflow, torch.
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Solid understanding of statistical analysis, data mining, and machine learning algorithms. Minimum of ten to twelve years of experience in data science, machine learning, or related roles.
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Masters degree in Earth Science, Geography, Remote Sensing/Geospatial Science, Computer Science or related field, or equivalent experience in a related technical field. Proven experience in applying machine learning and deep learning techniques to agricultural monitoring using frameworks such as PyTorch, TensorFlow, and Scikit-Learn.
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As a Staff Data Scientist, you will utilize your expertise in data mining, predictive modeling, machine learning, and NLP techniques to drive strategic decisions for WCS. You will be at the forefront of implementing models and strategies using A/B testing or Design of Experiments (DOE), tracking business performance, and providing insightful visualizations to support and continuously improve WCS business strategy.
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Much of our work contributes to innovative research in the fields of sensor science, signal processing, data fusion, artificial intelligence (AI), machine learning (ML), and augmented reality (AR.
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Evaluate commercial and open-source techniques in Machine Learning, Data Mining, NLP, and Analytics. Master's degree in Computer Science, Statistics, or Mathematics required with emphasis on data mining, NLP, or machine learning.
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Master's Degree in a quantitative discipline such as Epidemiology, Biostatistics, Economics, Statistics, Clinical Informatics, Mathematics, Data Science, Data Analysis and/or related fields.
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McKesson Technology Data & Digital Innovation org is seeking a Senior Data Scientist that is passionate about building machine learning applications that ensure patients receive the medications they need, when they need them; improve quality of care for oncology patients; and exemplify operational excellence so that the business can focus on what matters most, creating value for our customers.
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As a Data Scientist, reporting into the Head of Data & Strategy at TRKKN North America (NA), you’ll focus on building scalable machine learning and AI products to help evaluate and optimize the business impact of our clients’ marketing investment.
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Examples include Azure Machine Learning Studio, Google’s Vertex AI, IBM Watson Studio, Amazon SageMaker and open-source tools like Kubeflow. Should have knowledge of, but not expertise in, open-source high-code frameworks like PyTorch or TensorFlow, augmented AI and ML platforms, pretrained ML models and integrated AI PaaS tools.
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Here is an opportunity to join our company, a company whose solutions lie at the convergence of many exponentially accelerating innovative technologies: Cloud, edge computing and the Industrial Internet of Things (IoT); AI, machine learning and deep learning; Big Data and analytics; and 3D printing (additive manufacturing.
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The Cash App Quantitative Finance Team is an intellectually diverse team of machine learning modelers, statisticians, economists, data scientists, former strategy consultants, and data engineers focused on applying graph analytics, statistics, econometrics, and machine learning algorithms to solve business problems for executives within Cash App and the broader Block organization.
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The team As a Data Scientist & Machine Learning Engineer, you will work closely with cross-functional teams and other Hitachi group companies to develop and implement data-driven solutions that drive strategic decision-making and deliver large-scale digital transformation for our clients.
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Must have strong DS design knowledge and has experience in some ML/Ops/DevOps Knowledge of building machine learning models using Scikit-Learn, TensorFlow, Keras, or other open-source libraries.
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Leverage large-scale data processing technologies like Spark and Hive, and machine learning platforms to handle complex datasets. Develop and implement highly scalable classifiers and tools using machine learning, data regression, and rule-based models.
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