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Data science, machine learning, optimization models, PhD in Machine Learning, Computer Science, Information Technology, Operations Research, Statistics, Applied Mathematics, Econometrics, 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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Good understanding of machine learning, deep learning (including LLMs) and natural language processing and ability to optimize machine learning models to adapt to solving various kinds of issues.
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Provide independent data science, machine learning, and analytical insights using member, financial, and organizational data to support mission-critical decision-making for Compliance-Complaints.
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Experience leveraging complex data to drive business decisions, hands-on experience in data science methodologies (predictive analytics, machine learning, patient level data triggers) using R, Python, Databricks and deep knowledge of Qlik, PowerBI, Tableau for visualization.
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5+ years expertise with Machine Learning and/or Natural Language processing. Degree in Computer Science, Machine Learning, Data Science or related field, with expertise in knowledge representation.
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Your primary focus will be in applying machine learning and data mining techniques, performing statistical analysis, and building high quality analytic systems - deep learning, anomaly detection, natural language processing, recommendation systems, predictive analytics and information retrieval.
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Proven experience as an NLP Engineer, Machine Learning Engineer, or similar role, with a strong background in natural language processing and machine learning.
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Experience in Machine Learning and Deep Learning, including regression, classification, neural network, and Natural Language Processing (NLP), LLM. Experience in Text Analytics, developing different Statistical Machine Learning, Data Mining solutions to various business problems, and generating data visualizations using R, Python.
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2 years experience with Python, Spark, or related frameworks in AI, machine learning, data science, data engineering or similar context. 1 year experience with Natural Language Processing, Generative AI or related techniques for machine understanding of natural language (i.e., written text, omics data, or similar.
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Our consultants bring deep expertise in Data Science, Machine Learning, and AI. Our business value and leadership have been recognized by various market research firms, including Forrester and Gartner.
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We are seeking candidates with expertise in areas such as machine learning, deep learning, natural language understanding, and data engineering to contribute to our interdisciplinary applied data science curriculum.
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Python ecosystem preferred, R will be acceptable, machine learning libraries & frameworks (e.g., TensorFlow, PyTorch, scikit-learn) and familiar with data processing and visualization tools (e.g., SQL, Tableau, Power BI.
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Candidate has expertise in AI, machine learning, deep learning, statistical data processing, regression techniques, neural networks, decision trees, clustering, pattern recognition, probability theory and data science methods used to analyze data.
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We combine best of breed engineering practices, subject matter expertise, design services, computer science, and data analytics with innovative approaches in artificial intelligence and machine learning, AR/VR, R&D, and physics-based modeling and simulation providing tailored solutions addressing our customer’s unique threat requirements.
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It has the mission to explore, enable and exploit artificial intelligence, machine learning, natural language processing, image recognition and cognitive computing at scale for countless use cases across Alpha/CRD, Global Services, Global Advisors, Global Markets business lines, and corporate functions.
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machine learning natural language processing r python data engineering jobs Title: data science manager
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The summer had economists from around the globe embroiled in a debate about a possible recession coming in the next few years (or months). As of October 2022, the U.S. Labor Department data put the current inflation rate at 7.7%. The recent layoffs in the tech industry are just the first of what is soon to be a string of cutbacks by companies looking to save costs. For recruiters, this means freezes in hiring and fewer openings. It will also include the uphill task of finding the best candidates for them from the coming influx of recently laid-off job seekers. Now is probably a good time to brace for tough times in the next few years in the talent acquisition industry. To survive and thrive recruiting in a recession, here are some hard truths you will need to accept.
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Collaborative Recruiting: The Key to a Better Talent Acquisition Strategy
Talent acquisition is a multi-stage process where candidates undergo various application steps before getting hired. The unfortunate reality is that it is a labor-intense system, with the hiring manager and recruiter often handling all of the work on their own. Ask any one of them, and you will hear about the overabundance of applications and the demanding task of filtering through them to find the best candidates. The quality of talent suffers under the weight of all that work on one person's hands. It's not easy, but as many companies are starting to realize, there is a better way. The future of talent acquisition lies in collaborative recruiting!