Data Scientist with AWS Sagemaker Exp

Data Scientists

• Focused on replication, scaling, and supporting ML solutions across clients.
• Responsibilities:
• Deploy and operationalize models created by economists.
• Onboard new partners (80+ airline clients).
• Expand and refine existing solutions (e.g., reward improvements).
• Requirements:
• 4+ years of experience, degree in related field.
• Ability to become self-sufficient over time and eventually take over economist responsibilities.

Technical Stack & Tools

• Primary ML algorithms:
• Contextual Bandits (reinforcement learning)
• XGBoost (baseline predictive models)
• K-means clustering
• Linear regression
• Platforms:
• Amazon SageMaker (Studio)
• Redshift
• Snowflake
• Q for Business
• Languages:
• SQL
• Python

Use Cases & Deployment Strategy

• Primary use cases:
• Reinforcement learning for upgrade bidding.
• Example: Customer receives an upgrade offer suggest an alternative product/ancillary offer.
• Hospitality use case: Ancillary services offered at random models can optimize targeting.
• New partner onboarding:
• Begin with existing data (80 airline partners).
• Economist monitors and customizes model.
• Roll out partner by partner using shared framework.

Additional Notes

• Solutions must work in both B2B and B2C contexts.
• Human end-customers are always the recipient of offers, even in B2B partnerships.
• By 2025, all models will be built and supported using SageMaker libraries.

Must Haves :

Amazon SageMaker Studio for ML development and orchestration.

Algorithms like:

• Contextual Bandits used for real-time decision making (e.g., pricing, recommendations).
• XGBoost a high-performance gradient boosting algorithm, often used in tabular data for predictions.
• K-means Clustering for unsupervised segmentation or grouping tasks.
• Linear Regression for basic predictive modeling or as a baseline.