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We use cutting edge machine learning, data mining and optimization algorithms on this data. Experience working as a Machine learning engineer closely collaborating with data scientists.
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A minimum of Hands-On 5 years’ experience in Front-End/UI Automation using Karate/Selenium. ●A minimum of Hands-On 1 years’ experience in Front-End/UI Automation using Karate/Selenium.
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The ideal candidate has a strong software engineering background and problem-solving ability along with cloud computing (AWS) and data engineering skill set with prior experience on technologies such as Snowflake, Airflow, dbt, Kafka, Spark, Python, and Tableau.
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Data Structures,Algorithms, Design, Database, Scale, hands-on programming,CS Fundamentals, System Design, working with cross-functional teams,Experience building and maintaining software for scale , Designed or developed the product from the scratch +Team management, Program management, Manage Hiring, Roadmap.
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The selected candidate will be leading projects to develop new EHS systems in the Microsoft Dynamics CRM platform as well as building additional functionality into current systems to provide relevant metrics and data visualization as well as integration to various platforms.
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Focus on detail-orientation skills and capable of managing large data sets. Provide environmental planning knowledge and project management to help with environmental impact analysis, coordination on biological and cultural assessment support, CEQA/NEPA research and document production support, tribal fees to trust projects and coordination for other associated environmental projects.
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Ability to communicate with vendors who perform maintenance or upgrade work on the data center systems. Possess a basic understanding of the mechanical, electrical, and fire life safety systems used in a data center environment including, but not limited to the following: electrical distribution and layout, Transformers, PLCs, Generators, Switchgear, UPS systems, STS’, ATS’ PDU’s, Chilled Water Systems, CRAC/CRAH’s, Pre-Action Sprinkler Systems.
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Experience or knowledge in LLM and hands on fine tune experience is a big plus. Have a strong working knowledge of machine learning algorithms such as XGBoost, CNNs, etc. Hands on and can-do attitude, willing to learn new technologies.
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Proven capacity to work collaboratively with technical and non-technical team members on a wide range of threat intelligence projects, including the ability to identify, collect, and analyze multiple data sources to produce finished analytic products for a diverse group of clients.
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Manager, Cyber Risk & Analysis (Machine Learning) Capital One's commitment to Machine Learning has sponsorship from leadership and the Enterprise ML Program is at the heart of this effort, and is leading the way towards building responsible and impactful tools, platforms, and solutions that leverage ML and Generative AI.
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We're looking for our first audience data and insights manager, someone who can be a strategic and analytical collaborator and who can generate actionable data insights to guide the organization's efforts.
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Produce as-built models based on the scan files using Trimble REALWORKS or similar software and/or TEKLA Structures. The position requires a self-motivated, open-minded and dedicated individual who is willing to take on new challenges.
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We solve large-scale worldwide problems by providing persistent overhead services to help our soldiers remain safe in theater, provision rapid emergency communications for disaster relief efforts, enable search-and-rescue services to assist emergency response efforts, help anti-poaching to protect the world’s most endangered species and provide geospatial data to assist in slowing climate change.
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Experience with Machine Learning frameworks such as Tensor Flow or PyTorch. 3 – 5 Years with DevOps responsibilities with hands on utilization industry standard tools and workflows.
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As a Sr. Engineer on the Shoe Palace team, you will be helping to transform the way applications are built and data is processed at JD Finish Line. Supporting a new platform on which applications will be built, leveraging a microservices architecture, Apache Kafka, Spring Boot, Docker, and Kubernetes.
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