Lead - Data Scientist

Job Summary

• Design and deploy machine learning models using Azure Machine Learning for customer segmentation, churn prediction, lifetime value estimation, and recommendation systems.
• Analyze historical customer data to uncover behavioral patterns and actionable insights.
• Build and optimize recommendation engines using collaborative, content-based, and hybrid approaches.
• Develop and evaluate targeted marketing campaigns using predictive analytics and personalization strategies.
• Operationalize ML workflows using Azure MLOps (CI/CD, model versioning, monitoring, retraining).
• Collaborate with data engineering to build scalable data pipelines using Azure Data Factory and Azure Synapse Analytics.
• Integrate models with real-time systems using Azure Functions, Event Grid, and API Management.
• Present insights and model outcomes to stakeholders through dashboards and storytelling using Power BI.
  

Required Skills / Must-Have

• Technical Skills: Python, SQL, scikit-learn, XGBoost, TensorFlow, PyTorch
• Azure Services: Azure Machine Learning, Azure Synapse Analytics, Azure Data Factory, Azure Cognitive Services, Azure Functions & Logic Apps, Azure
• Experience: 5+ years in data science with focus on customer analytics and personalization
• Platforms: Customer Data Platforms (Adobe Experience Platform, Salesforce CDP, Segment)
• MLOps Tools: MLflow, Azure ML Pipelines, DVC
• Data Warehousing: Snowflake or equivalent
• Statistical Methods: A/B testing, causal inference, statistical modeling
  

Nice-to-Have / Preferred Skills

• Industry Experience: Airline, travel, or retail industry
• Architecture: Real-time personalization and event-driven architectures using Azure Event Hubs or Kafka
• Deployment: FastAPI or Flask integrated with Azure API Management
  

Education & Qualifications

Secondary / Acceptable Alternatives Education

• Azure AI Engineer Associate (preferred)
• Azure Data Scientist Associate (preferred)
• MLflow or DVC certification (preferred)
  

• Customer Analytics: Customer segmentation / churn prediction / LTV modeling
• Recommendation Systems: Collaborative filtering / content-based / hybrid
• Azure AI: Azure ML / Synapse / Data Factory / Cognitive Services
• MLOps: CI/CD / model monitoring / retraining / ML pipelines
• Real-time Systems: Azure Functions / Event Grid / API Management