Data Scientist II at LexisNexis applying statistical analysis and building predictive models for fraud and credit risk. Collaborating with teams to enhance existing products and provide actionable insights.
About the role
- Senior Data Scientist at Accelya optimizing pricing models for airlines. Involving demand forecasting, model deployment, and cross-functional collaboration.
Responsibilities
- 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
Experience level
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Not specified
Degree requirement
Tech skills
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