Data Scientist

• Build and maintain demand-forecasting and marginal-revenue models used to produce opportunity costs (bid prices) at route/flight/segment granularity.
• Derive customer segments with clustering, embeddings, and rule-based approaches that are predictive of purchase behavior.
• Develop conditional choice / purchase-probability models that control for endogeneity. Design and interpret natural or randomized experiments where applicable, using IVs, control-function approaches, double ML, or structural methods as needed.
• Integrate forecasted demand, choice probabilities and bid price constraints into an optimization layer (deterministic optimization, dynamic programming, or gradient-based methods).
• A/B/Experimentation & measurement: design online/offline evaluation frameworks and randomized experiments to validate price strategies, measure revenue impact, and control risk.
• Production & MLOps: deploy models and optimizers into low-latency production pipelines (APIs/real-time scoring), implement monitoring for model performance, price sensitivity drift and KPI alerts.
• Cross-functional delivery: communicate results and trade-offs to RM/product/stakeholders and translate business requirements into model constraints and instrumentation.

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