Analyst within Credit Risk Management team identifying credit segmentation opportunities using statistical methods. Collaborating with teams to enhance credit decision process and policies.
About the role
- 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.
Requirements
- 4+ years industry experience building demand forecasting, pricing, or choice models for e-commerce, travel, retail, or similar.
- Strong applied econometrics / causal inference skills (experience with IVs, double ML, or structural estimation).
- Experience with discrete choice / purchase probability models (MNL, nested logit, or neural networks) or demonstrably equivalent approaches.
- Hands-on experience building forecasting pipelines (classical and ML approaches) and producing demand or marginal revenue estimates.
- Experience exposing ML models and optimization as production services (low-latency inference) and implementing monitoring/alerts.
- Strong coding skills in Python.
- Familiarity with cloud platforms and tools: AWS (S3, EC2, SageMaker), Databricks/Spark, Airflow, and MLflow or similar.
- Experience designing and analyzing A/B tests and uplift experiments; strong statistical hypothesis testing skills.
- Excellent communication: can explain causal assumptions, model limitations, and pricing trade-offs to RM and product stakeholders. Fluent English: Interviews will be held in this language.
Benefits
- Health insurance
- Paid time off
- Flexible working arrangements
- Professional development
Job title
Job type
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Not specified
Degree requirement
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