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At the heart of this mission is our data science team, which plays a crucial role in developing and enhancing machine learning-based detection methods to identify and malicious actors, ensuring the protection of our customers.
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Expertise in deep learning, statistical processing, regression techniques, decision trees, clustering, pattern recognition, probability theory and data science methods and the mathematical theories underlying these data tools.
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You should have in depth experience in machine learning or data science, and have already trained and developed multiple advanced models in the recommender, NLP, or related areas.
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Experience with Machine Learning, Statistics and Probability, NLP, Deep Learning especially experience in recommendation systems, conversational systems, information retrieval, computer vision, regression modeling.
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You will work closely with cross-functional teams, including engineering, design, data science, and analytics, to up-level experimentation practices and culture by creating standardized frameworks and tools that evolve our existing systems.
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The ideal candidate will have a strong background in Data Science, Deep Learning, and Neural Networks. 3+ years of experience in Data Science, Deep Learning, and Neural Networks.
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QBE is on the lookout for a Lead Machine Learning Engineer with deep expertise in developing and deploying advanced machine learning models and solutions. Certified Specialist in Predictive Analytics (CAS) or other data science related certifications.
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Proficient coding skills using Python, Spark, Tensorflow/PyTorch, and other deep learning tools. A bachelor's degree is required, with a strong preference for fields of study in computer science, statistics, and data mining.
$129,200 - $194,750 a yearFull-timeExpandApply NowActive JobUpdated 2 days ago - UpvoteDownvoteShare Job
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6+ years of proven experience in Data Science, predictive modeling using Statistics and Machine Learning. The team is part of Investment Risk Management Group and supports multiple investment risk managers (across Equity, Fixed Income and Alternatives) by developing data science solutions and machine learning techniques.
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A deep technical background in applied data science, spanning some or all statistics, experimentation, machine learning, causal methods, optimization techniques, data engineering and architecture (applied to large data), and behavioral analytics.
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Familiar with deep learning platforms (e.g., Keras, Tensorflow) and related classification and segmentation algorithms. Proficient at machine learning algorithms, familiar with algorithms like Generalized Linear Model, Random Forest, CART, and/or deep learning.
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Familiarity with deep learning and machine learning algorithms and the use of popular AI/ML frameworks. Bachelor’s degree or several years of experience in computer science, data science, software development, or another related field; a master’s degree is recommended.
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You have a deep knowledge of Python, and a strong understanding of modern deep learning, natural language processing, and the inner workings of the transformer architecture. Advanced degree in Data Science/CS/EE/Applied Mathematics/Statistics/ML/NLP or related fields and/or relevant and equivalent engineering experience.
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It enables early detection of issues at various levels of the stack via advanced time-series forecasting and anomaly detection using novel deep learning techniques. You will have the opportunity to build a career solving network security challenges leveraging advanced machine learning techniques on exabytes of data.
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Apply machine learning, deep learning, and NLP methods to massive data sets and complex systems. Investigate and solve exciting and difficult challenges with emerging technology and data science.
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