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Working remotely from home or any other work location. Demonstrate experience with high-level languages and frameworks such as R, Python, Perl, Ruby, Scala, Apache Spark, Storm.
$104,000 - $150,000 a yearTemporaryRemoteExpandApply NowActive JobUpdated Today - UpvoteDownvoteShare Job
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A deep understanding of data science and machine learning best practices, development processes, and leading platforms (Databricks, SAS, Anaconda, Alteryx, etc.) 2+ years’ experience in machine learning (ML), artificial intelligence (AI), and/or natural language processing (NLP) preferred.
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Experience with Machine Learning cloud technologies such as Azure Machine Learning (AML), AWS SageMaker or Spark ML. Experience with Big Data platforms such as Databricks or Apache Spark.
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We are hiring talented Senior Data Engineers in roles related to building cloud-based data pipelines for machine learning, data processing with Apache Spark, and database development.
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Analyzes data using data mining, machine learning, and statistical algorithms available in COTS tools (e.g. SAS, SPSS, and Oracle); builds analytical solutions using programming languages (e.g. R, Python, SAS), and programming libraries (e.g. Python SciKit, R Caret, PostgreSQL MADlib, Apache Spark MLlib.
$76,224.72 - $113,633.52 a yearFull-timeExpandApply NowActive JobUpdated 4 days ago - UpvoteDownvoteShare Job
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Expertise in Python and SQL, with working experience in Apache Spark, Hadoop, Databricks, Snowflake, or other big data systems. Experience with next generation architectural concepts, including Integrations, advanced analytics, cloud analytics, big data, artificial intelligence, and/or machine learning.
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Experience with high speed distributed computing frameworks such as AWS EMR, Hadoop, HDFS, S3, MapReduce, Apache Spark, Apache Hive, Kafka Streams, Apache Flink etc. Past/Current experience of 3+ years working on a large scale cloud native project (platform: Unix/Linux, Type of Systems: event-driven / transaction processing / high performance computing) as Technical Lead. These experiences should include developing / architecting core libraries or framework used by the platform to support fundamental services like storage, alert notifications, security, etc.
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We are looking for an experienced Backend Software Engineer, Senior Staff level, to join our Cargo team composed of everyone from deep-learning to econometrics experts. You thrive on building data solutions and analytics tools, drawing actionable conclusions and digging deeper to uncover trends that will improve our machine learning systems.
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Proficiency in Apache Spark, including Spark Core, Spark SQL and Spark Streaming. Use of tools like Apache Airflow, ADF, or Databricks to orchestrate and schedule data workflows.
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Python ecosystem preferred, R will be acceptable, machine learning libraries & frameworks (e.g. TensorFlow, PyTorch, scikit-learn) and familiar with data processing and visualization tools (e.g., SQL, Tableau, Power BI.
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More than 10,000 organizations worldwide — including Comcast, Condé Nast, Grammarly, and over 50% of the Fortune 500 — rely on the Databricks Data Intelligence Platform to unify and democratize data, analytics and AI. Databricks is headquartered in San Francisco, with offices around the globe and was founded by the original creators of Lakehouse, Apache Spark, Delta Lake and MLflow.
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3 years of experience in the past 4 years in working with years of experience in big data technologies such as Apache Hadoop, Apache Spark, or Apache Kafka. 3 years of experience in the past 4 years in working with Microsoft Azure Cloud platform, including familiarity with other Azure services like Azure Data Lake Storage, Azure Databricks, Azure Data Factory, and Azure DevOps.
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Collaborate with backend engineers and data engineers to integrate the frontend application seamlessly with data pipelines and workflows on top of modern data processing technologies such as Apache Spark and Databricks, to support the real-time processing and visualization of large-scale data.
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Experience working in Data engineering and ETL teams and on managing implementation projects that utilize big data, advanced analytics and machine learning technologies. Big Data technologies, Azure, AWS, Hadoop, Spark, Hive, Kafka, Flume, NoSQL stores (HBase, Cassandra, DynamoDB, MongoDB.
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What's so great about working on Calendly's Data Science & Machine Learning team? You will report to the head of Data Science & Machine Learning and will be responsible for driving new initiatives using the latest advancements in ML, working closely with cross-functional teams, and helping to drive business insights & growth as well as creating magical experiences for our end customers through innovation.
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