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Hands-on production experience with data pipeline orchestration systems such as Airflow for creating and maintaining data pipelines. Implementation shared libraries in Scala and Python that abstract complex business logic to allow consistent functionality across all data pipelines across the Data organization.
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Experience with cloud computing platforms (, AWS, Azure, GCP) and cloud native computingProficiency in programming languages like Python, Java, Scala, C#, SQLExpertise in big data technologies such as Hadoop, Spark, Kafka, and NoSQL databasesKnowledge of data modeling, ETL processes, and data warehousing conceptsExceptional problem-solving abilities and teamwork skills.
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5+ years hands-on experience with the Big Data stack (HDFS, SPARK, MapReduce, Hadoop, Sqoop, Pig, Hive, Hbase, Flume, Kafka) Hands-on experience with "productionalizing" Big Data applications (e.g. administration, configuration management, monitoring, debugging, and performance tuning.
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Hands-on production experience with orchestration systems such as Airflow. Architect, design, and code shared libraries in Scala and Python that abstract complex business logic to allow consistent functionality across the Data organization.
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5+ years of experience in test automation and testing integration functions same or like RCSA related business controls Proven hands-on experience in Java, JEE, Streaming, API, Microservices, Containerization and UX/UI tools such as Angular JS/React, etcIn-depth knowledge on data engineering, DB models and architecture in RDBMS, Big Data and other cloud databases such as snowflake, Databricks, etc.
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Hands-on experience with big data technologies and cloud (Databricks / Spark, Azure/AWS/GCP) Comfortable with command line (Linux, Windows) scripting Experience with at least one other programming language (Python, R, Julia, Scala, Go, Java, C.
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You have hands-on experience developing data science solutions using open-source tools and cloud computing platforms. Leverage a broad stack of technologies - Python, AWS, Pyspark, Nvidia Rapids / Merlin / Triton, and more - to reveal the insights hidden within huge volumes of numeric and textual data.
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In-depth knowledge on data engineering, DB models and architecture in RDBMS, Big Data and other cloud databases such as snowflake, Databricks, etc. Proven hands-on experience in Java, JEE, Streaming, API, Microservices, Containerization and UX/UI tools such as Angular JS/React, etc.
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Strong knowledge of SQL, Python, data-structured languages, and big data. Hands-on experience building statistical, machine learning, and deep learning models.
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Hands on exposure to data science tools & techniques such as Big Data, PySpark, Hive, Scala, Python is required. Proficiency & experience in applying cutting edge statistical and machine learning techniques to business problems and leverage external thinking (from academia and/or other industries) to develop best in class data science solutions.
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Hands on experience in using SQL, Python and Workflow Schedulers (Apache Airflow, Cron) Experience with Big Data Technologies (Hadoop, Hive, Hbase, Pig, Spark, etc.
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Big data / AWS Cloud / Micro services. Strong Hands-On Experience with Spring framework (preferably Spring Boot). Strong Hands-On Experience with Core Java, J2EE, JMS.
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Strong Hands-On Experience with SQL, PL/SQL programming. Strong Hands-On MUST Experience of a minimum of 2 years with React. Strong Hands-On Experience with Rest APIs.
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As a Data Scientist at Capital One, you’ll be part of a team that’s leading the next wave of disruption at a whole new scale, using the latest in computing and machine learning technologies and operating across billions of customer records to unlock the big opportunities that help everyday people save money, time and agony in their financial lives.
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Spark using Scala / python/java and HDFS. All applicants will be evaluated solely on the basis of their ability, competence and their proven capability to perform the functions outlined in the corresponding role.
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