AZURE & Machine Learning experts

eClerx

6 - 11 years

0 Lacs

mumbai pune

Posted:1 month ago| Platform:

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Skills Required

azure machine learning machine learning ml algorithms data bricks python azure synapse pyspark time series microsoft azure forecasting

Work Mode

Work from Office

Job Type

Full Time

Job Description

Role & responsibilities

1. Data Analysis and Model Development

Conduct detailed audits of CRM, application, and enrolment datasets in Azure to assess data quality, completeness, and suitability for modelling.
Design, develop, and implement machine learning models to forecast student numbers across multiple intakes, covering variations by course, mode, location, and student type.
Apply advanced statistical and machine learning methods (e.g., ARIMA, Prophet, Gradient Boosting, Random Forest, LSTM) to maximise forecast accuracy, interpretability, and reliability.
Validate, test, and refine models using recognised performance metrics such as RMSE, MAPE, and R to achieve required accuracy standards.

2. Integration and Automation

Integrate forecasting models into existing Azure-based data pipelines, leveraging Azure Data Factory, Synapse, and Data Lake services.
Automate model training and deployment cycles following MLOps and CI/CD best practices (using MLflow, DVC, GitHub Actions, and Azure DevOps).
Embed model outputs within Power BI dashboards and other visualisation tools for business and academic users.
Develop scalable and maintainable solutions to ensure forecasts can be efficiently retrained and updated.

3. Knowledge Transfer and Documentation

Collaborate with university stakeholders through virtual workshops, progress meetings, and hands-on knowledge transfer sessions.
Produce comprehensive technical documentation detailing data structures, model logic, retraining procedures, and maintenance guidelines.
Deliver training sessions for internal staff to ensure sustainable in-house management of forecasting models post-deployment.

4. Communication and Collaboration

Communicate technical concepts clearly and effectively to non-specialist audiences in fluent English.
Collaborate across international time zones using Microsoft Teams, SharePoint, or equivalent collaboration platforms.
Provide regular progress updates, insights, and reports on model performance, along with recommendations for continuous improvement

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