Yield Management Specialist – GTM, Data Scientist

Key Responsibilities

• Model Development & Analytics:
  Design, train, and evaluate machine learning models, including predictive, classification, and ensemble methods, and conduct exploratory data analysis to surface trends, anomalies, and decision-support signals.
• AI Application & LLM Integration:
  Build and integrate LLM powered workflows for insight generation and decision support, blending structured business metrics with external signals through effective prompt engineering and harness in the agent.
• Data Pipeline & Engineering:
  Design, build, and maintain scalable ETL and data pipelines across multi-source datasets to power analytics, reporting, and downstream applications.
• Data Products & Visualization:
  Develop interactive analytics applications and dashboards (such as Dash/Power BI) that deliver real-time analytics, KPI monitoring, and actionable business insights.
• Model Evaluation & Data Quality:
  Establish model evaluation frameworks grounded in statistical metrics and business KPIs, and safeguard data reliability through validation of completeness, consistency, and ongoing pipeline monitoring.
• Collaboration & Delivery:
  Partner with data engineers, software engineers, and product owners to translate business needs into robust analytic deliverables, balancing technical rigor with speed to delivery.

• Bachelor’s degree in a quantitative field, such as Data Science, Statistics, Computer Science, Mathematics, or an equivalent combination of relevant education and experience.
• 3+ years of hands-on experience applying Python and SQL to data analysis and machine learning.
• Solid understanding of core machine learning algorithms, statistical methods, and model evaluation techniques.
• Demonstrated experience working with both structured and unstructured data.
• Master’s degree in a quantitative field, such as Data Science, Computer Science, Statistics, or Mathematics (even better).
• Experience with cloud platforms (such as Google Cloud Platform, AWS, or Azure) for analytics and model deployment (even better).
• Exposure to Generative AI, Large Language Models (LLMs), prompt engineering, or AI agent frameworks (even better).
• Familiarity with data pipeline and engineering tools (such as PySpark, Airflow, or Big Query) (even better).
• Experience with data visualization tools (such as Power BI, Tableau, or Dash) (even better).