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Mid-level, common ML/Azure Data Scientist role with metro appeal increases applicant competition.
Strong ML and Azure specialization limits cross-industry transferability, increasing background sensitivity.
Explicit 4+ years requirement plus mandatory ML, deep learning, Azure, and MLOps skills raise strictness.
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Develop, deploy, and monitor scalable machine learning models for diverse business use cases including classification, regression, forecasting, and NLP.
Build and maintain data pipelines and ensure data quality using Python and Microsoft Azure services (Azure Data Factory, Databricks, Data Lake, Synapse).
Apply AI and Generative AI technologies on Azure (Azure OpenAI, Cognitive Services) to deliver intelligent applications and automate workflows.
Bachelor's or Master's degree in Computer Science, Data Science, Engineering, Mathematics, or related field.
At least 4 years of professional or project-based experience in data science or machine learning.
Proficiency in Python and hands-on experience with Microsoft Azure ML ecosystem (Azure ML, Databricks, Data Factory, Blob Storage, Synapse).
Experience with deep learning frameworks (TensorFlow or PyTorch) and solid SQL skills.
Experienced in end-to-end ML lifecycle including model building, deployment, monitoring, and retraining in production environments on Azure.
Knowledgeable in MLOps best practices such as experiment tracking, model versioning, and CI/CD for ML.
Familiar with AI/GenAI integration and comfortable collaborating with data engineering teams to maintain data governance on cloud platforms.