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Proficient in big data frameworks such as Flink (preferred), Hadoop, Beam, Spark, etc. Hands-on implementation of streaming data processing engine. Set technical direction for streaming data processing engine.
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The Lead Data Engineer is responsible for orchestrating, deploying, maintaining and scaling cloud infrastructure targeting big data and platform data management (e.g., data warehouses, data lakes) including data access APIs. Prepares and manipulates data using Hadoop or equivalent.
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Apache Hadoop or Apache Spark (for big data processing) Data Warehousing (e.g., AWS Redshift, Google BigQuery) Natural Language Processing (NLP) Tools and Frameworks (e.g., Hugging Face, AWS Comprehend Medical for extracting insights from clinical text data.
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Experience with Big Data frameworks (e.g. Hadoop MapReduce, Spark, Hive, Kafka) Expertise with Google Cloud Platform’s distributed Machine Learning, Data Science and Data Engineering tools (e.g. BigQuery ML, VertexAI, AutoML, Docker, Kubernetes, Kubeflow, Dataproc.
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Minimum of 7 year of designing and building production data pipelines from data ingestion to consumption within a hybrid big data architecture, using Cloud Native GCP, Python, SQL etc.
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Utilize Databricks for big data processing, machine learning workflows, and data analytics. The ideal candidate will have deep expertise in building and deploying Python microservices using FastAPI, along with strong experience in Azure, SQL, Kafka, Databricks, and GCP (Google Cloud Platform) data engineering technologies.
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The ideal candidate will have a strong background in Collibra Data Governance and a foundational understanding of big data platforms like Databricks. Foundational experience with big data platforms, particularly Databricks.
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Good understanding of landscape of AI solutions - cloud, GPU based compute, data security and privacy, API gateways, microservices based architecture, big data ingestion, storage and processing.
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Docker, Kubernetes, ECS, EKS or AWS Fargate (for containerization and orchestration of data applications and reproducibility) Data Visualization Tools (e.g., Tableau, Power BI, Plotly) The Data Scientist will be responsible for driving insights from the vast amounts of patient and environmental data available within our data warehouse.
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HIPAA Compliance (handling sensitive patient data securely) Data Privacy (understanding of privacy laws such as HIPAA, GDPR) Work closely with researcher teams to design analysis specifications, including input data specifications, data cleaning, algorithms, and interpretation of results.
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Leveraging cutting-edge single-cell and spatial multi-omics technologies, cellular imaging, and innovative bioinformatics tools and computational frameworks, we aim to harness the potential of big data to fight cancer.
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Data Scientist (Healthcare) 100% Remote. Data Science & Machine Learning Frameworks. Python (for preprocessing, data analysis, machine learning, scripting) Data Governance & Security.
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Develop and implement algorithms on existing data warehouse records and identify new external data sources to be ingested to the data warehouse to strengthen analyses. Research and implement AI algorithms, apply off-the-shelf AI and data-centric tools, and collect, store, and maintain data.
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Auditing & Compliance Tools (for ensuring secure and compliant data handling) SAS (common in healthcare data analysis) Data Anonymization or De-identification techniques (for research and compliance.
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As a Data Scientist, reporting into the Head of Data & Strategy at TRKKN North America (NA), you’ll focus on building scalable machine learning and AI products to help evaluate and optimize the business impact of our clients’ marketing investment.
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big data jobs Company: Capgemini in AK, West Virginia
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