End-to-End Data Scientist: Azure ML & BI Impact

SGL Manila (Shared Service Center), Inc.; Muntinlupa; On-site; PHP 1,200,000 - 1,800,000; Full time

Job summary

SGL Manila (Shared Service Center), Inc. is seeking a data scientist to work on the Microsoft data platform, from framing business questions to deploying, monitoring, and presenting results to non-technical stakeholders in logistics, finance and commercial.

You will build features and data pipelines with Python/SQL, deploy models in Azure ML and MLflow, monitor data quality and drift, and collaborate with the AI team on evaluating large language model solutions while upholding data governance

Qualifications

• At least three years applying machine learning and statistics to business problems.
• Degree in data science, statistics, computer science, mathematics or a related quantitative field.
• Microsoft Azure Fabric or related certifications are advantageous.

Responsibilities

• Frame business questions with stakeholders, then build, validate and deploy models in Azure Machine Learning and Microsoft Fabric.
• Engineer features and data pipelines over lakehouse and warehouse data using Python and SQL.
• Put models into production with MLflow and managed endpoints, and monitor drift, data quality and performance.
• Communicate findings through Power BI and written analysis to clearly inform non-technical stakeholders.
• Partner with AI team on evaluating large language model solutions and ensure data governance and privacy.

Skills

Python

SQL

Feature engineering

Model deployment

Education

Master's or bachelor's degree in data science

Tools

Azure Machine Learning

Microsoft Fabric

MLflow

Power BI

Job description

SGL Manila (Shared Service Center), Inc. is seeking a data scientist to work on the Microsoft data platform, from framing business questions to deploying, monitoring, and presenting results to non-technical stakeholders in logistics, finance and commercial.

You will build features and data pipelines with Python/SQL, deploy models in Azure ML and MLflow, monitor data quality and drift, and collaborate with the AI team on evaluating large language model solutions while upholding data governance