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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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We employ scalable cutting-edge machine learning (ML), causal inference (CI) and GenAI / Natural Language Processing (NLP) knowledge to better target customers and prospects, understand and personalize the content, and context needed to optimize their book-listening experience.
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You should have experience using deep learning, natural language processing, or machine learning systems. A little about ADP: We are a global leader in HR technology, offering the latest AI and machine learning-enhanced payroll, tax, HR, benefits, and much more.
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Audible Content Data Science team partners with business, technology, and product leaders to solve problems related to Content Understanding, Content Evaluation, and Recommendations, relying on Natural Language Processing (NLP), ML, Deep Learning, LLMs/GenAI. We operate in an agile environment in which we own the life cycle of research, design, model development and, often, deployment.
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Experience using statistical analysis and computing, machine learning, deep learning, processing large data sets, data visualization, data wrangling, mathematics, and programming.
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Previous experience in a technical role such as a data scientist, solutions architect, machine learning engineer, AI consultant, etc. You will drive the delivery and adoption of novel data science approaches across medical imaging, natural language processing/generation (NLP/NLG), autonomous systems, edge-AI, graph neural networks, and other state-of-the-art applications.
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Experience applying a range of analytic approaches, including (but not limited to) machine learning, text analytics, and natural language processing; graph theory, link analysis and optimization models; complex adaptive systems; and/or deep learning neural networks that are part of the exploration.
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Understanding of machine learning algorithms (e.g. k-NN, GBM, Neural Networks Naive Bayes, SVM, and Decision Forests), statistical analysis, and data visualization techniques; Staying current with the latest advancements in data science and machine learning.
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