Full Time Azure Data Scientist information
See salary details
$81.9K - $99.9K
4% of jobs
$99.9K - $117.8K
9% of jobs
$133.1K is the 25th percentile. Wages below this are outliers.
$117.8K - $135.8K
11% of jobs
$135.8K - $153.7K
7% of jobs
The median wage is $159.5K / yr.
$153.7K - $171.7K
50% of jobs
$171.7K - $189.6K
2% of jobs
$189.6K - $207.6K
1% of jobs
$207.6K - $225.5K
0% of jobs
$225.5K - $243.5K
13% of jobs
How much do full time azure data scientist jobs pay per year?
As of Aug 17, 2026, the average yearly pay for full time azure data scientist in the United States is $165,018.00, according to ZipRecruiter salary data. Most workers in this role earn between $133,500.00 and $170,000.00 per year, depending on experience, location, and employer.
A Full Time Azure Data Scientist leverages Microsoft Azure's cloud-based services and tools to collect, analyze, and interpret large datasets for organizations. They design and implement machine learning models, build data pipelines, and deploy solutions using Azure Machine Learning, Databricks, and related technologies. Their work helps companies make data-driven decisions, optimize business processes, and drive innovation by harnessing the power of cloud-scale data analytics.
A Full Time Azure Data Scientist requires expertise in statistics, machine learning, and data analysis, typically supported by an advanced degree in a quantitative field. Proficiency with Microsoft Azure services (such as Azure Machine Learning, Data Factory, and Databricks), programming languages like Python or R, and relevant certifications (e.g., Microsoft Certified: Azure Data Scientist Associate) are highly valued. Strong communication, problem-solving, and collaboration skills help differentiate top performers in this role. These skills and qualifications are essential for designing effective data solutions, ensuring smooth deployment in cloud environments, and aligning insights with business objectives.
Full Time Azure Data Scientists often encounter challenges related to data quality, consistency, and compatibility when integrating data from diverse sources such as on-premises databases, cloud storage, and third-party APIs. Ensuring secure data transfer, maintaining compliance with data governance policies, and managing large-scale data processing are also frequent hurdles. Leveraging Azure services like Data Factory, Synapse Analytics, and Data Lake can help streamline these processes, but success requires close collaboration with data engineers and IT teams to resolve integration issues efficiently.
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