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BS, MS, or PhD degree in Computer Science, Data Science, Economics, Statistics, Mathematics, Physics, Computational Biology or equivalent experience. You have a strong background in data science, machine learning, graph mining, and graphical modeling algorithms.
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Data processing technologies and streaming workflows (e.g., Spark, Kafka, Flink, Beam) Tackle novel challenges in blockchain, data science, and threat intelligence. Data pipeline and workflow orchestration tools (e.g., Airflow, DBT, Luigi, Azkaban, Storm.
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The adjacent areas of major focus are Machine Learning Infrastructure and workflow, Experimentation Platform, Knowledge Graphs and various Data Science and Analytics related tooling. Apache Kafka, Apache Flink, Apache Spark, Trino (Presto), Apache Airflow/Dagster, Apache Superset, AWS S3, Snowflake, Amplitude, CDP (e.g. Segment.com.
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Data Engineering technologies (Ex: Spark, Hadoop, Kafka)Expertise in one of the following:Data Science and Machine Learning technologies (Ex: pandas, scikit-learn, pytorch, Tensorflow.
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Diagnostics is creating a data analytics and science team to generate Client insights and real world evidence (RWE) from real world data (RWD) that contain lab results generated by DHR Dx OpCo instruments linked to patient medical records.
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This implies, among others, writing software in Scala/Java, building data pipelines (Apache Spark, Apache Kafka), integrating with third-party applications, and interacting with cloud APIs (AWS, Azure, CloudFormation, Terraform.
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We are seeking a highly skilled Director of Data Science & Analytics to shape our company's data-driven decision-making. A track record of designing and executing data science roadmap and delivering data products and insights that influence engineering and business decisions.
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Knowledge of machine learning and data science concepts. Develop and optimize Spark applications for data processing and analysis. Proven experience with Azure Databricks and Apache Spark.
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10+ years of experience in data engineering on distributed frameworks such as Spark, Databricks, Kafka Streams, Hadoop etc. You will have the opportunity to collaborate with the data science and analytics team to develop high performance analytics, data model and ML solutions.
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Leverage Spark, Airflow, Apache Beam, Google Kubernetes Engine, BigQuery and other tools to build robust and efficient data pipelines. Deep experience with data frameworks and tools like Spark, Spark Streaming, Apache Beam, and Airflow.
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You’ll work closely with our customers, Product Managers, ML Engineers, and leadership team to perform deep data science and develop sophisticated statistical and ML modes that provide insights on network dynamics and their impacts on various metrics in supplier networks and product value chains.
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On the Aurora Data Science team, you will join a world-class group whose mission is generating insights and intelligence to improve the Aurora Driver and help accelerate our path towards safe and broad commercialization of Autonomous Vehicle technology.
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Deep expertise in provisioning and managing data infrastructure components like Kafka, Spark, and Airflow. Oversee the provisioning and deployment of infrastructure using Pulumi, ensuring seamless deployment of Kafka, Spark, Airflow, Athena, and other critical systems on AWS.
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Proficiency and hands-on knowledge in a variety of technologies such as SQL, Bash, Python, Java, Presto, Spark, AWS, data streaming like Kafka, RabbitMQ. Bachelor's or Master's degree in Computer Science or equivalent, with over 12 years of Data Architecture and Data Engineering experience, including team leadership.
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In depth expertise with streaming data components Kafka, Spark, Flink, Hadoop, Presto/Trino, Redis. Deep understanding of streaming data components, including Kafka, Spark, Hadoop, Presto/Trino, Flink and Redis.
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spark data science jobs Company: Intuit Inc in San Francisco, CA
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