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Apply computational tools and machine learning/deep learning techniques to analyze and interpret complex biological data relevant to drug discovery. Strong proficiency in programming (e.g., SQL, Python, R, MATLAB), database technologies (Oracle, mySQL, relational databases; graph databases are a plus), machine learning/deep learning (network architectures are a plus), dimensionality reduction techniques (e.g., PCA), and possible cheminformatics software suites.
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Join the Anheuser-Busch NAZ Tech Data and Analytics Team as a Machine Learning Engineer. Leverage data science frameworks like TensorFlow, PyTorch, and Spark ML to build robust and scalable machine learning solutions.
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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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This candidate should be proficient in using BI tools such as Tableau, in addition to experience with one or more modern technologies such as relational databases, AWS, Snowflake, Python, Databricks, Git, and Machine Learning.
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Professional Certification (Security+, Data+, Cyber Security Analyst+, or Systems Security Certified Practitioner) Center 3 (19075), United States of America, McLean, VirginiaPrincipal Associate, Cyber Security Data Scientist.
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Candidates aiming to integrate modern methods of data science (DS), machine learning (ML) and artificial-intelligence (AI) to accelerate advances in their respective research areas are encouraged to apply.
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Cyber Data Science (CDS) is looking for a data-focused professional with cyber security experience to join our new and growing team. This candidate will operate as a team lead as CDS builds new data-driven systems to identify trends in our Cyber landscape and implement proactive actions.
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Under the direction of the IT Management, will establish standards, policies and procedures pertaining to data governance, database/data warehouse management, metadata management, security, optimization, and utilization.
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Experience in working with large, heterogeneous datasets in building and optimizing data pipelines, pipeline architectures, and integrated datasets using traditional and modern data integration technologies (such as ETL, ELT, MPP, data replication, change data captures, message-oriented data movement, API design, stream data integration and data virtualization.
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Principal Associate, Cyber Security Data Scientist. Bachelor's Degree in Computer Science, Cyber Security, Engineering, Data Analytics, or related field. As part of this, you may be tasked to work with various teams across Cyber Operations, Engineering, Product, and Architecture to optimize our data delivery and extraction across multiple data sources.
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Bachelor's or Master’s degree in Computer Science, Data Science, Machine Learning, or a related field. The Vertex AI Machine Learning Engineer will be responsible for developing, deploying, and managing machine learning models using Google Cloud's Vertex AI platform.
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Leveraging a blend of cybersecurity and data science knowledge to identify patterns and relevant insights that drive decision-making at operational and strategic levels. Regularly leveraging existing cyber security knowledge to create relevant insights that facilitate data-driven decisions.
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Leading initiatives to incorporate Machine Learning into projects and BAU activities. Able to programmatically extract, transform, visualize, and analyze data from databases or S3. In this role, you will be responsible for interacting with cyber security related data to create and communicate insights powering reports and real-time dashboards that create situational awareness and facilitate data-driven decisions.
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Industry certifications and/or experience with cloud platforms and modern data center solutions such as AWS, GCP, Dell, HPE, NetApp, Nutanix, Hitachi, IBM, Etc. Demonstrates technical leadership and subject matter expertise on Cloud architectures and offerings in the modern data center.
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The Research Data Scientist participates in biomedical research projects using programming, data-mining, statistics, machine learning, and visualization techniques to develop, evaluate, and/or apply algorithms and software for data analysis.
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