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The CTFM Data Science Engineer will collaborate and execute data strategy and analytics and data UI/UX across the entire portfolio of Medidata's CTFM solutions, many of which rely on the core product platform capabilities and services.
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We are seeking exceptional candidates with interests in a wide range of topics within the theoretical, machine learning, data visualization, and computational foundations of data science.
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Artificial intelligence, recruitment, machine learning, data science, staffing, ai, community, podcast, Computer Vision, Deep Learning, data engineering, big data, automation, ServiceNow, splunk, digital transformation, executive search, data analytics, cybersecurity, software engineering, software development, and it.
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It plays a major role in developing and delivering Data Exploitation (Data Science, Data Analytics) and Artificial Intelligence as crosscutting enabling capabilities, and facilitates NATO’s evolution into a digitally transformed, data centric, and Multi-Domain Operations (MDO)-enabled Alliance by 2030.
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BS degree in Data Science, Computer Science, Information Sciences, Mathematics, Engineering or related field and minimum 6 years directly related data analytics, data science, predictive modeling, machine learning, statistical modeling and/or user experience role.
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Fluency with Python machine learning and data science packages (Scikit-learn, Apache Spark, PySpark, DASK, Tensorflow, etc.) Strong technical expertise in data science, data analysis, statistical modelling, and/or machine learning.
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Bachelor's Degree in GIS, Computer Science, Geography, Social Science, Environmental Science, Data Science, Engineering, or related field. A successful candidate will work both independently and in close coordination with various AECOM staff and project teams to develop, deploy and support GIS and other Geospatial Data Science critical to successful project delivery.
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Works with the Center Director's office and directly with Branch Chiefs, Research Managers, Scientists and/or Data Specialists/Data Scientists to provide data management policy guidance and technical support for USGS science efforts within the Center/Office.
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As this is part of multiple ongoing collaborations, the nature of the work will depend on your strengths and experiences; this is an exciting opportunity to be involved in work with direct real-world impact and leverage our department's expertise to build skills in a number of areas from high-performance computing/GPU acceleration, implementation of machine learning algorithms, computational biology pipelines, matrix operations on sparse data, among others.
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If you are passionate about data science, possess a strong analytical mindset, and thrive in a collaborative environment, we encourage you to apply. Collaborate closely with other data scientists, analysts, brand marketing teams, centralized sales and marketing activation teams, and data engineering/architecture teams to define, scope, and deliver comprehensive data-driven products and solutions aligned with multiple priorities.
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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, Supervisory experience, Using open source frameworks (for example, scikit learn, tensorflow, torch.
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Engaging and collaborating with all branches of HQ SACT and identifying, executing and implementing analytic opportunities for data analytics, data science and AI to include business intelligence and robotic process automation (RPA.
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There are various disciplines involved in Enterprise Data & Analytics, including: data source identification and analysis, data engineering, data visualization & data science/machine learning.
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An advanced degree (master’s or doctoral) in a quantitative field, including but not limited to Computational Biology, Computer Science, Data Science, Applied Mathematics/Statistics, all relevant engineering and science disciplines.
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Languages: Familiarity or proficiency in languages commonly used in data science, like Python, R, or Scala. Containerization: Familiarity with Docker, Kubernetes, etc., for scalable deployment of data science applications.
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data science jobs Title: consultant Company: Ctg
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