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ManTech is seeking a highly motivated and career-oriented Data Scientist Subject Matter Expert to help lead the development and delivery of high-quality predictive modeling solutions for our CBP customer.
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Algorithm Development (25%) Implement machine learning, statistical, data-mining, bioinformatics/clinical informatics algorithms. Dr. Ma works on computational causal discovery and predictive modeling theory and method development.
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The Research Scientist will be a key team member, eliciting stakeholder needs and generating requirements, developing operational concepts and use cases, researching emerging technologies and state of the art practices, designing analyses, modeling, and simulations, and evaluating the application and trade-offs of various technologies.
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Business Intelligence (BI), Database Design, Data Engineering, Data Modeling, Data Science, Data Visualization, Machine Learning, Project Management, Software Development, Stakeholder Relationship Management.
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2+ years of professional experience as a Machine Learning Engineer, Data Scientist, or equivalent role, focusing on machine learning, statistical modeling, and/or ML infrastructure.
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Provide data modeling, mining, pattern analysis, data visualization and machine learning solutions to address customer needs. The position includes: analytic computing, data analytics, Machine Learning/predictive modeling and communication of solutions and results.
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Systems, predictive modeling, etc.) Execute statistical and data mining techniques ( hypothesis testing, machine learning and retrieval processes) on large data sets to. Knowledge of big data/advanced analytics concepts and algorithms.
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2 year experience as data scientist or academic work related to Machine Learning / Artificial Intelligence. Structured Finance & Lending (SFL) Strats Team within Global Markets Division (GMD) is responsible for modeling and pricing of structured trades as well as creating monitoring and management tools for Structured Lending Businesses and Clients.
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Proficient in software engineering, with the ability to develop large scale and low latency infrastructure for deep learning data, modeling, and system evaluation. This position is for a Principal Data Scientist with strong Machine Learning System development experience, who will chip in to both strategic and tactic planning and execution to continuously advance AI Platform's vision and mission.
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Identifies and/or develops innovative and effective approaches and methodologies; applies advanced operations research and data science techniques for quantitative analysis, statistical analysis, forecasting, predictive modeling, prescriptive analysis, and optimization; and validates analysis and outcomes.
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The candidate should have expertise in data modeling, SQL, Python, R, data warehousing, big data technologies, data pipeline construction, data quality, cleansing, version control, cloud services, machine learning, feature engineering, deep learning, and natural language processing.
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Knowesis is seeking a skilled Data Scientist to join the team in our joint venture supporting the Department of Defense (DoD) Office of Inspector General (OIG). Experience with Cloudera Data Platform, Cloudera Data Science Workbench (includes R, SparklyR, PySpark, Spark), Cloudera Data Flow (NiFi), and Tableau for analysis and modeling.
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Working closely with senior data scientists and cross-functional teams, you'll have the opportunity to learn and grow while gaining hands-on experience in data analysis, machine learning, and predictive modeling.
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In particular, the Data Scientist will be responsible for extracting relevant data, modeling business processes, discovering business insights, and identifying opportunities through the use of in-depth statistical, algorithmic, data mining and advanced visualization techniques.
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PenFed is hiring a (Hybrid) Data Scientist, Credit Modeling at our San Antonio, Texas location. Consumer Lending credit/fraud underwriting risk modeling. Autonomous end-to-end statistical model creation, including but not limited to identifying objectives, compiling data, sampling/prepping data, feature selection, model comparison/selection, deployment, and monitoring.
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