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Job Qualifications: How You Qualify Bachelor of Science (or higher) degree in Meteorology, Computer Science, or a related field, or at least 10 years of relevant experience in computer/systems administration, data operations, and related engineering.
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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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Quantum Computing, Quantum Information Processing, Quantum Algorithms, Quantum Applications, Quantum Physics, Optics, Photonics, Artificial Intelligence, Machine Learning, Software, Hardware, and Systems Engineering.
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Plus, to be a Committer or PMC Member with the Apache Software Foundation on big data technologies: Apache Spark, Hadoop Stack, Kafka, Comet, Atlas, delta. Recognized Subject-Matter Expert big data engineering and cloud-based data warehouse.
$154,000 - $278,800 a yearFull-timeExpandApply NowActive JobUpdated 6 days ago - UpvoteDownvoteShare Job
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Finance, Business Administration/Management, Economics, Statistics, Computer and Information Science, Management Information Systems, Engineering, Data Processing/Analytics/Science, Mathematics, Accounting.
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Utilizing knowledge of modern big data approaches preferred: e.g. PySpark, Apache Spark, Azure Databricks, Azure SQL, Azure ML, R, Python, Java, etc. The PwC Deal Analytics practice is a blend of deals and consulting professionals with a diverse set of skills and backgrounds including financial, commercial, operational and data science.
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A proven track record in building and maintaining big data platforms for streaming and batch data processing, data engineering, building backend systems and APIs. Solid background in the fundamentals of computer science, distributed systems, concurrency, resiliency, caching, large scale data processing, database schema design and data warehousing.
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Working knowledge of performing data ETL (Extract, Transform and Load), and experience with SQL, Alteryx, Power Query and other agile technologies; Leveraging business intelligence software (e.g. Tableau, PowerBI, Qlik, etc.
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Our products are trusted by leading companies including PayPal, Visa, FTX, Uniswap, Anchorage, and federal agencies such as the FBI and IRS. Every day, we tackle challenges in data engineering, data science, and threat intelligence to advance our mission to build a safer financial system for billions of people.
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Utilizing previous experience and track record of data extraction/transformation, analytics, and visualization approaches; Managing high performing technical teams, comprising data scientists, database administrators, data analysts, and / or data visualization specialists, etc.
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Perform system design efforts and software implementation of proof-of-concept prototypes incorporating a variety of engineering/scientific disciplines: software development, applied mathematics, data science, electrical engineering, embedded microcontroller/microprocessor programming, network/web services, ML/AI concepts, operating systems, pattern recognition, RF engineering, and visualization.
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Analyzing or managing accounting, financial, transactional data, and statistical methods in support of data analysis; Each client, deal and situation is unique and the ability to translate data into actionable insights for our clients is crucial to our continued success.
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A proven track record in building and maintaining big data platforms for streaming and batch data processing, data engineering, building backend systems and APIs. As a Senior Software Engineer - Data Platform and Products at Branch, we are looking for an ambitious, self driven individual who is at home on a PB scale data platform to join our growing data platform, products and engineering team.
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Software Engineering & DevOps:Version Control: Familiarity with tools like Git. Continuous Integration/Continuous Deployment: Knowledge of pipelines and tools like Jenkins, Travis CI.Containerization: Familiarity with Docker, Kubernetes, etc., for scalable deployment of data science applications.
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As part of the Data Analytics team, the Lead Data Scientist will work closely with the Data Engineering team and business functions to solve real-world oil and gas midstream problems using machine learning, data science algorithms and artificial intelligence.
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