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Fluency with Python and data analysis using modern frameworks for deep learning Pytorch, TensorFlow-Keras). The incumbent will apply advanced data analysis skills and machine learning in collaboration with a group of computational and biological scientists to perform translational oncology research around multimodal translational data sets for programs in clinical development.
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Familiarity with Snowflake, Monte Carlo, RDS, DynamoDB, Kafka, Fivetran, dbt, Airflow, Docker, Kubernetes, EMR, Sagemaker, DataDog, PagerDuty, Atlan, Data Observability tools and Data Governance tools.
$235,000 a yearFull-timeExpandApply NowActive JobUpdated Today - UpvoteDownvoteShare Job
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You'll also become an expert on modern development trends, especially the cloud-native stack, devsecops, Gitops, and modern data-engineering pipelines. Craft data-engineering pipelines (using tools like BigQuery) to identify security vulnerabilities before they've been exploited.
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We enable organizations of all sizes to easily build, scale, and run modern applications by helping them modernize legacy workloads, embrace innovation, and unleash AI. Our industry-leading developer data platform, MongoDB Atlas, is the only globally distributed, multi-cloud database and is available in more than 115 regions across AWS, Google Cloud, and Microsoft Azure.
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Experience with modern data stack like Airflow/Astronomer, Fivetran, Tableau/Looker. Experience with data warehouses like BigQuery, Databricks, Snowflake, and Postgres. Design, develop, and own robust, scalable data processing and data integration pipelines using Python, dbt, Kafka, Airflow, PySpark, SparkSQL, and REST API endpoints to ingest data from various external data sources to Data Lake. Develop testing frameworks and monitoring to improve data quality, observability, pipeline reliability, and performance Orchestrate sophisticated data flow patterns across a variety of disparate tooling.
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Experience with modern technology service architectural hosting, security, and risk management concerns (e.g. Data Warehouse, Data Engineering Pipeline, Data Preparation and Modeling, Advanced - Analytics, Data Services, and Consumption.
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Preferred Skills Strong understanding of modern GPU architectures and programming frameworks like CUDA or OpenCL.Experience working with NVIDIA Data Center-class GPUs (A100, H100, etc)Experience with deep learning frameworks like TensorFlow or PyTorch is a plus.
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Coactive was founded by experts who shaped the fields of high-performance deep learning and data-centric AI. We have the scars from building and working with the first generation of modern machine learning systems at Google, Meta, Pinterest, eBay, Lyft, and other leading organizations.
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You will find yourself at home if you are comfortable with work such as scaling Kubernetes services, debugging Kafka consumer lag, diagnosing various distributed systems failures, and developing a new end-to-end data processing pipeline, all the way from raw data capture to bespoke analytics leveraging a data warehouse or a stream processing framework.
$440,000 a yearFull-timeExpandApply NowActive JobUpdated Today - UpvoteDownvoteShare Job
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Experience with distributed large-scale data processing (preferably Apache Spark). Fluency in at least one modern object-oriented programming language (preferably Java/Kotlin and Python.
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As we partner with Oracle Cloud engineering teams, you will participate in advancement and early adoption of cutting edge OCI Big Data, data lake and data warehouse services and products.
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By combining world-class transaction expertise with proprietary data and advanced analytics, RPX obtains patent rights that avoid or resolve patent-related disputes shared among our diverse global memberships and provides unique insights that inform our members’ strategic and tactical executive decisions.
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Technical: You have hands-on experience developing data science solutions from concept to production using open source tools and modern cloud computing platforms. 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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Experience with modern data processing stacks, DBT, Dagster, Airflow. Deep understanding of statistics, data science, or large scale data processing. What you'll be doingDesign, build, and support Discord's next generation experimentation platform: scope of work would include core services, data pipelines, configuration tooling, and UX.Collaborate with external stakeholders and customers throughout the company to ensure experiments are well run, and their results are accurately understood.
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We're pioneering a new class of risk-free property management by using modern data science to efficiently model risk, while deploying tech-powered operations to streamline consistent, high-quality customer service.
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