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They should have experience applying linear algebra, differential equations, dimensional reduction, machine/deep learning methods, and data mining and visualization to applied problems and geospatial data; be able to code in the Python or R language; and be capable of developing documentation and manuscripts for submission to peer-reviewed academic outlets.
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Hands-on experience of machine learning methodologies including advanced analytics tools (such as R and Python) along with applied mathematics, ML and Deep Learning frameworks and libraries (TensorFlow, PyTorch, Keras) and ML techniques.
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Adhere to and contribute to the development of best practices for statistical modeling, machine learning, natural language processing, information retrieval, data mining, and big data management and analytics.
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Experience in Data Science, Data Mining, Machine Learning, Predictive Modeling best practices. Experience in Data Science, Data Mining, Machine Learning, Predictive Modeling best practices.
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4 years of experience in managing, and mentoring data science, data engineering, and/or analytics teams in the financial services industry. 4 years of experience in managing, and mentoring data science, data engineering, and/or analytics teams in a highly regulated environment.
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Drive the adoption of data science-driven mechanisms and machine learning models to continuously evaluate and improve catalog data quality. Techniques: Statistical Analysis, Visualization, Optimization, Machine Learning, Big Data, Data Warehouse, NLP.
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MS, or PhD in Computer Science, Computational Mathematics, Applied Mathematics, Engineering, Data Science, Statistics or equivalent. We look for Data Scientists who are passionate about building and deploying machine learning models, have experience with the full stack end-to-end AI lifecycle, exposure to Generative AI and Large Language Models, and are great communicators.
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Utilization of embedding models and Vector DBs to transform and store natural language data. Background investigation components can vary dependent upon specific assignment and/or level of US government security clearance held.
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Understanding of Data Engineering, Data Science, Machine Learning, and AI workflows and their optimization. Fingerhut fuses our extensive years of data modeling and real-time analytics through machine learning to provide flexible payment options for our valued customers looking to establish, build, or rebuild credit.
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Experience in structural health monitoring (SHM), non-destructive evaluation (NDE), and non-invasive testing,+ Experience in electrical measurements, building of complex experimental setups, and sensor development,+ Experience in advanced signal processing, including machine learning,+ Proficiency in software development using Python, MATLAB, and LabVIEW,+ Experience in modeling & simulation studies using COMSOL,+ Ability to plan and organize assignments to meet project deliverables.
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Methods vary widely but include applied mathematics, deep learning, machine learning, probability modeling, Bayesian reasoning, quantum computing, spatial statistics, spatio-temporal modeling, decision support, and data mining.
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Basic to substantial experience in one or more of the following commercial/open-source data discovery/analysis platforms: RStudio, Spark, KNIME, RapidMiner, Alteryx, Dataiku, H2O, SAS Enterprise Miner (SAS EM) and/or SAS Visual Data Mining and Machine Learning, Microsoft AzureML, IBM Watson Studio or SPSS Modeler, Amazon SageMaker, Google Cloud ML, SAP Predictive Analytics.
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We are on the forefront of CBRN defense and we are looking for talented Data Scientists that have applied experience in the fields of artificial intelligence, machine learning and/or natural language processing to join our team.
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Velvetech constantly follows the Tech trends and actively cooperates with startups in such breakthrough areas as Machine Learning, the Internet of Things, Blockchain, FPGA, and AI.
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As an Applied Ontologist, you will help to extend and refine our ontology and taxonomy to embrace new vocabulary, increase the accuracy of classifications to better guide semantic search, identify and evaluate new sources of reference data and help to wed them to the knowledge graph.
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