[Hiring] Sr. Engineer Data Scientist – Dynamic Pricing & Revenue Optimization @Accelya US Inc

Role Description

The ideal candidate combines a strong foundation in applied statistics and machine learning with experience in modeling consumer behavior, price sensitivity, and real-time optimization. You will play a central role in bringing data science innovation into production at scale. This role is highly cross-functional: you will partner with product, engineering, RM/Revenue teams, and airline customers.

• 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.

Qualifications

• 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. Comfortable with ML stack: scikit-learn, XGBoost/LightGBM/CatBoost, PyTorch/TensorFlow optional.
• 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.

Requirements

• Prior experience in airline revenue management, dynamic pricing, retail offer optimization, or hospitality pricing.
• Experience with discrete choice estimation libraries or packages, or research experience in choice modeling.
• Advanced degree (MSc/PhD) in econometrics, statistics, economics, operations research, or applied ML is a plus.
• Familiarity with NDC, ATPCO concepts, or airline shopping/PNR/ticketing data formats.
• Experience with optimization libraries and/or reinforcement learning for pricing.
• Publications or internal technical reports on pricing, elasticity estimation, or choice modeling are a plus.

Benefits

• Work at the intersection of data science and aviation, tackling real-world challenges that impact global airlines.
• Opportunity to work with large, complex datasets and apply state-of-the-art machine learning techniques.
• Collaborate with a highly skilled team of data scientists, engineers, and business leaders.
• Your work will directly influence how airlines optimize their pricing, forecast demand, and maximize revenue.
• Opportunity to apply your skills on a global scale.