Data Scientist/Engineer

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October 16

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Cayuse Holdings

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Government • B2B • Cybersecurity

Cayuse Holdings is a company that represents a portfolio of businesses focused on delivering IT services across various sectors, including Government, Commercial, and Native Solutions. The company prioritizes long-term relationships with clients and provides a wide range of services, such as program and project management, cybersecurity operations, and systems implementation. With a commitment to sustainability and stewardship, Cayuse Holdings aims to make a positive impact while servicing federal, state, local governments, and tribal entities.

501 - 1000 employees

🏛️ Government

🤝 B2B

🔒 Cybersecurity

📋 Description

• Build and Maintain Data Pipelines: Develop and manage scalable data pipelines using Azure Data Factory, Azure Synapse Analytics, or Azure Databricks to process large volumes of data. • Data Quality and Transformation: Ensure the transformation, cleansing, and ingestion of data from a wide range of structured and unstructured sources with appropriate error handling. • Optimize Data Storage: Utilize and optimize data storage solutions, such as Azure Data Lake and Blob Storage, to ensure cost-effective and efficient data storage practices. • Collaboration with ML Engineers and Architects: Work with Machine Learning Engineers and Solution Architects to seamlessly deploy machine learning models into production environments. • Build automated systems to monitor model performance, detect model drift, and trigger retraining processes as needed. • Ensure reproducibility of ML experiments by maintaining proper version control for models, data, and code. • Ingest, explore, and preprocess both structured and unstructured data with tools such as Azure Data Lake Storage, Azure Synapse Analytics, Azure Data Factory.

🎯 Requirements

• Proven experience in building and managing data pipelines using Azure Data Factory, Azure Synapse Analytics, or Databricks. • Strong knowledge of Azure storage solutions, including Azure Data Lake and Blob Storage. • Familiarity with data transformation, ingestion techniques, and data quality methodologies. • Proficiency in programming languages such as Python or Scala for data processing and ML integration. • Experience in exploratory data analysis and working with notebooks like Jupyter, Azure Machine Learning Notebooks, or Azure Databricks. • Solid understanding of machine learning lifecycle management and model deployment in production environments. • Strong problem-solving skills with experience detecting and addressing data anomalies.