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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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Experience with Machine learning, Natural Language Processing and Vector Search. The candidate will be working with the following state of art technologies; Solr, Lucene, Natural Language Processing, Machine Learning, Linux, Groovy, Python, Splunk, Prometheus, Grafana, DevSecOps, Jenkins, Maven, Gitlab, Nexus, Ansible, TDD, BDD, JMeter, Selenium, and other open source frameworks.
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KEYWORDS Machine Learning | Physics | Embedded Systems | Signal Processing | Data Analysis | Nvidia | Electrical Engineering | Cloud Computing | Cuda | TensorRT | Radio Frequency | Transmission Processing | Linux OS | Pytorch | Python | Edge Computing.
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This role is a unique opportunity for an experienced ML scientist and hands-on NLP/Gen AI/ LLM senior scientist to grow into the next step in their career journey and apply her or his domain expertise in NLP, deep learning, GenAI, and LLMs to drive business value for multiple stakeholders while mentoring and growing a ML Data Science team.
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Promote modern approaches in Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), Large Language Models (LLMs) within the computational and data ecosystem to advance researcher productivity and scientific discovery.
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Land IQ, LLC is seeking a Remote Sensing Scientist in its Sacramento, California office with a specialization in data driven analytics and algorithm development with digital images for agricultural and other land use applications (crop classification, yield modeling, land use impacts, land use change, etc.
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We are looking for a passionate Software Engineer, Big-Data Engineer, Machine-Learning Engineer, Full-stack Engineer, or UI/UX Engineer, who can contribute and make a difference in any of the different components of our Knowledge Graph Platform.
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Sitting at the intersection of quantitative and investment analysis, the professional will be a "hands-on" player/coach building a team of Data Scientists focused on the implementation of Data Science technologies, techniques and methodologies leveraging Artificial Intelligence (AI) and Machine Learning, advanced analytics, and statistical methods.
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The Principal Data Scientist at CVS Health spearheads innovative strategies, leveraging advanced analytics and consultative problem-solving to drive incremental membership and margin for Aetna and Caremark in the dynamic landscape of the Healthcare and Pharmacy Benefit Management (PBM) sectors.
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The ideal candidate should possess a strong understanding of search engine marketing (SEM), excellent data analytical skills, and the ability to stay updated on industry trends. Experience in Search Engine Marketing (SEM) and Paid Search (PPC.
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SSIS strongly preferredPython DBT, Mulesoft, Alation – good to havePrimary Responsibilities:Strong understanding or Snowflake on Azure Architecture, design, implementation and operationalization of large-scale data and analytics solutions on Snowflake Cloud Data Warehouse.
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Experience in Data science and Machine learning using Python, R, Java, C#, Spark, AutoML, TensorFlow, Amazon AML, Microsoft machine learning studio, PyTorch, IBM Watson and any graph DB.
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Big data/machine learning and AI techniques, including neural network and deep learning frameworks and machine learning tools (e.g., TensorFlow, Theano, Torch, Keras.
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Conduct site identification for utility scale solar, wind and battery storage projects across the PJM Mid-Atlantic region using ArcGIS, Google Earth and other available data. Business Developer will develop utility-scale solar, wind and battery storage projects with a team of experts to support Avangrid's growing pipeline and goals.
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Deep hands-on experience in machine learning, relational databases, and open source programming languages (Python, R) for large scale data analysis. Strong knowledge of data science technologies and machine learning concepts and have experience developing models from research, to a proof of concept, to a production pipeline.
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