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Popular mid-level ML role in Bangalore with broad required skills increases competition.
Specialized forecasting and MLOps requirements create high domain bias and limited industry transferability.
Multiple mandatory technical requirements including 3+ years, forecasting, Databricks, Azure, and Kubeflow.
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Lead design and deployment of production-grade forecasting and predictive models using statistical and ML techniques.
Develop and maintain software infrastructure including Python/R code, R Shiny apps, Kubeflow pipelines, and cloud resources on Azure.
Monitor and troubleshoot full stack issues including data pipelines, model degradation, and implement continuous evaluation and alerting systems.
Master's or PhD degree with 3+ years of experience delivering end-to-end data science production solutions.
Strong programming skills in Python, R, and SQL.
Expertise in time series forecasting methods and data engineering using Databricks/Spark/PySpark and Delta Lake.
Experience with MLOps tools like Kubeflow, CI/CD pipelines (GitHub Actions, Azure DevOps), and Azure cloud services.
Senior data scientist comfortable owning complex forecasting projects end-to-end including feature engineering, deployment, and monitoring.
Experienced in both software engineering best practices and cloud infrastructure management within Azure ecosystem.
Capable of autonomous work translating ambiguous business requirements into scalable and testable technical solutions.