Data Scientist with AWS Sagemaker Exp

Data Scientist with AWS Sagemaker Exp

Posted 2 weeks ago by 1752911408

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Negotiable
Outside
Remote
USA
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Algorithms AWS SageMaker Boosting Gradient Boosting K-Means Clustering Linear Regression Machine Learning Machine Learning Algorithms Predictive Modeling Python (Programming Language) Reinforcement Learning SQL (Programming Language) Xgboost

Summary: The Data Scientist role focuses on replicating, scaling, and supporting machine learning solutions for over 80 airline clients. Key responsibilities include deploying models created by economists, onboarding new partners, and refining existing solutions. The position requires a strong background in machine learning algorithms and tools, particularly with Amazon SageMaker. Candidates should have at least 4 years of experience and the ability to become self-sufficient in their role over time.

Key Responsibilities:

  • Deploy and operationalize models created by economists.
  • Onboard new partners (80+ airline clients).
  • Expand and refine existing solutions (e.g., reward improvements).

Key Skills:

  • 4+ years of experience, degree in related field.
  • Ability to become self-sufficient over time and eventually take over economist responsibilities.
  • Proficiency in Amazon SageMaker, SQL, and Python.
  • Experience with primary ML algorithms such as Contextual Bandits, XGBoost, K-means clustering, and Linear regression.

Salary (Rate): undetermined

City: undetermined

Country: USA

Working Arrangements: remote

IR35 Status: outside IR35

Seniority Level: undetermined

Industry: IT

Detailed Description From Employer:

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.
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