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Advanced and hands on experience using: Python, Databricks, Azure ML, Azure Cognitive Service, Ads Data Hub, BIQuery, SAS, R, SQL, PySpark, Numpy, Pandas, Scikit Learn, TensorFlow, PyTorch, AutoTS, Prophet, NLTK.
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Leverage data science frameworks like TensorFlow, PyTorch, and Spark ML to build robust and scalable machine learning solutions. Hands on experience working on data science frameworks such as TensorFlow, PyTorch, and Spark ML.
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You will be responsible for developing, validating, and implementing cutting-edge machine learning algorithms, including use of Large Language Models (LLM) applied to diverse healthcare data sources, e.g. electronic medical records and to generate medical reports.
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Data science, machine learning, optimization models, Master’s degree 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)Doctorate, Masters: Artificial Intelligence.
$117,000 - $234,000 a yearFull-timeExpandApply NowActive JobUpdated Today - UpvoteDownvoteShare Job
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Requirements: Advanced degree (Ph. D. preferred) in a field like Applied Linguistics, Computational Linguistics, or Data Science, with a focus on building and deploying machine learning models for text analysis.
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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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Having experience with productizing machine learning models within a cloud service (e.g., AWS, Azure, GCP) and data science environments (e.g., Databricks) is a plus. You have 2-5 years of successful technical experience in the domain of predictive analytics (e.g. data science, machine learning, data mining, and statistics related work); preferably in the Oil & Gas industry.
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Expertise in NLP and machine learning frameworks (e.g., TensorFlow, PyTorch). Bachelor's or Master's degree in Computer Science, AI, Machine Learning, or a related field.
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Our core offerings Bidder-as-a-Service (BaaS) and the Beeswax DSP provide clients with agility, custom optimizations, and extensible APIs. We are looking for a Machine Learning Engineering with an in-depth working knowledge of the latest in Machine Learning/Data Science technologies and cloud platforms to join our Optimization team, which have three defining pillars: machine learning, software engineering, and machine learning operations (MLOps.
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We work closely with various machine learning (ML) applied science teams dedicated to improving the quality of models that drive semantic matching, ranking, computer vision, image processing, and augmented reality.
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Conducting experiments on state-of-the-art machine learning models using Tensorflow for real world, large scale problems. Conducting experiments on state-of-the-art machine learning models using Tensorflow for real world, large scale problems.
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Publications or experience in machine learning, AI, computer vision, optimization, computer science, statistics, applied mathematics, or data science. We are seeking individuals passionate in areas such as deep learning, computer vision, audio and speech processing, natural language processing, machine learning, reinforcement learning, computational statistics, and applied mathematics.
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The ideal candidate will have a strong background in data science, programming, and machine learning algorithms and will be passionate about leveraging data to drive business decisions.
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Demonstrated expertise in machine learning, deep learning, and NLP, with a strong portfolio in large language models like GPT-3, BERT, and RAG.Advanced proficiency in Python, with hands-on experience in ML libraries and frameworks such as TensorFlow and PyTorch.
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As a hands-on leader, stay up front with latest technology development in the science domain, including but not limited to machine learning, generative AI and LLMs, and drive science applications and innovations in products and programs in SDS.
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