Urgent! Sr. Engineer Data Scientist – Dynamic Pricing & Revenue Optimization Job Opening In WorkFromHome – Now Hiring Accelya
Sr. Engineer Data Scientist – Dynamic Pricing & Revenue Optimization page is loaded## Sr. Engineer Data Scientist – Dynamic Pricing & Revenue Optimizationremote type: Hybridlocations: Colombia - Remotetime type: Full timeposted on: Posted Todayjob requisition id: JR For more than 40 years, Accelya has been the industry’s partner for change, simplifying airline financial and commercial processes and empowering the air transport community to take better control of the future.
Whether partnering with IATA on industry-wide initiatives or enabling digital transformation to simplify airline processes, Accelya drives the airline industry forward and proudly puts control back in the hands of airlines so they can move further, faster.
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.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.Required Skills:· 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.Preferred:· 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.At Accelya, you will work at the intersection of data science and aviation, tackling real-world challenges that impact global airlines.
You will have the opportunity to work with large, complex datasets, apply state-of-the-art machine learning techniques, and 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.
If you have a background in revenue management, dynamic pricing, or cloud-based AI/ML, you will have an opportunity to apply your skills on a global scale.
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Great news! Accelya is currently hiring and seeking a Sr. Engineer Data Scientist – Dynamic Pricing & Revenue Optimization to join their team. Feel free to download the job details.
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