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AWS cloud services such as EC2, EMR, RDS, Redshift, Glue, SageMaker (or Azure and GCP equivalents) Data streaming systems: Storm, Spark-Streaming, etc. Data science tools/languages: R, R Studio, Python (data preparation and analysis libraries), Databricks, etc.
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The Data Scientist role will have a significant role in the design and development of Aireon’s Machine Learning applications and products as well as supporting data analysis of the performance of the Aireon space-based Automatic Dependent Surveillance-Broadcast (ADS-B) air traffic surveillance system.
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Traffic engineering operational analysis (VISSIM, Synchro, HCS) Developing design plans including signing & pavement marking, traffic signal design, and communications. Exposure to traffic analysis software (VISSIM, Synchro.
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Conduct research on a variety of transportation engineering projects, which encompass traffic simulation, CDA, Digital Twin, V2X, CV, safety, and Intelligent Transportation Systems (ITS). 2+ years of hands-on experience in professional or academic setting with at least one of microscopic traffic simulation tools (e.g., SUMO, Vissim, Aimsun, Transmolder, or similar.
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Performing various aspects of Transportation design as directed by a licensed Transportation Engineer or Senior Transportation Engineer including analysis and design involving interstates and interchanges, urban and rural roadways, multimodal improvements, traffic engineering, site grading, storm water management, erosion control, and site plan preparation, and design related to a business development activity such as a response to a Request for Proposal (RFP.
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Intermediate Work Zone Traffic Control - VDOT Approved Provider. Pavement Marking - VDOT. Hydraulic Cement Concrete Field - VDOT or NICET LEVEL II* Soil and Aggregate Compaction - VDOT or NICET LEVEL II.
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Our high-performing team works with clients to implement the full spectrum of data analytics and data science, ranging from data architecture design, data engineering and querying, data visualization and dashboarding, predictive analytics, machine learning, and artificial intelligence.
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Design, develop, and implement data architecture solutions using SQL Server, Microsoft Azure, Snowflake, and Databricks. The ideal candidate will have extensive experience in designing and implementing data solutions, with proficiency in SQL Server, Microsoft Azure, Snowflake, Databricks, and other relevant technologies.
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As required, you will participate in the development and fielding of operational and analytical tools and techniques that enhance the organization’s ability to ingest, collate, search and tailor UAS related data to meet customer requirements.
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Proficiency in data analysis tools (Python, R), machine learning frameworks (TensorFlow, PyTorch), and data visualization tools (Tableau, Power BI). Strong knowledge of healthcare data standards, terminologies (ICD-10, SNOMED), and electronic health records (EHR) systems.
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Responsible for data engineering functions including, but not limited to data extract, transformation, loading, integration in support of modern cloud computing platforms like Azure and Databricks.
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Utilize programming languages like SQL, Python and tools like databricks, iceberg, presto, graphql and Cloud based data technologies. The Senior Lead Data will be responsible for partnering with Technology, Machine Learning, Product Architecture, and other Capital One teams to support the development of the Finance Tech Data Strategy into products and services we create and consume.
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Utilizes sophisticated analytics tools to continually monitor and optimize marketing program outcomes, including strategic allocation of resources and program adjustments based on detailed analysis of historical performance data and results.
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Experience in data center power system design, including microgrid design. Knowledge of power electronics fundamentals and various power conversion designs for data center power delivery and power converter modeling and simulation.
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Collaborate closely with business stakeholders to ingest and analyze existing data dictionaries, business glossaries, and taxonomies to determine the existence of metadata gaps, alignment with controlled vocabularies, and potential contribution to increase usability, reliability, and search/discoverability of data assets associated with the Collibra adopters’ use cases.
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