Amsterdam
2 days ago
Machine Learning Scientist
Booking.com
Amsterdam
2 days ago
Machine Learning Scientist
Booking.com is seeking a Senior Machine Learning Scientist in Amsterdam to design, build, and deploy uplift models and causal inference systems for promotional spend allocation. The role requires strong expertise in causal inference, neural network design for tabular data, and production ML, with opportunities to publish applied research.
Core AIHybridFull-timeSeniorCausal InferenceUplift Modeling
Machine Learning Scientist
Booking.com is seeking a Senior Machine Learning Scientist in Amsterdam to design, build, and deploy uplift models and causal inference systems for promotional spend allocation. The role requires strong expertise in causal inference, neural network design for tabular data, and production ML, with opportunities to publish applied research.
Core AIHybridFull-timeSeniorCausal Inference
Salary
Not specified
Core Qualifications
Technical (Must-have)
Causal InferenceUplift ModelingTreatment Effect EstimationPythonTensorFlowPyTorchLightGBMXGBoostExperimental DesignA/B TestingStatistical MethodologyApache SparkApache AirflowNeural Network DesignProduction ML Pipelines
Soft Skills
CommunicationCross-functional CollaborationMentoringCoachingTechnical LeadershipStakeholder Communication
Preferred Qualifications
Technical (Nice-to-have)
Heterogeneous Treatment EffectsInterference / Spillover EffectsPolicy LearningNeural Network Design for Structured/Tabular DataEmbeddingsAttentionMulti-task Architectures
Key Responsibilities
- Design and deploy uplift models that estimate heterogeneous treatment effects, optimising incremental return on investment under budget constraints.
- Design and execute causal inference methodologies; including observational debiasing (IPW, doubly robust estimation), sensitivity analysis, and interference-aware evaluation to close the gap between offline metrics and online impact.
- Advance the team’s neural network architectures for uplift modeling on tabular data (attention mechanisms, multi-head designs, self-supervised pretraining), balancing model expressiveness with production latency requirements.
- Research marketplace interference and cannibalization; building frameworks to measure and correct for demand shifting when partial treatment is applied across competing properties.
- Develop offline evaluation methods that reliably predict online performance, accounting for biases introduced by non-stationary treatment policies and interference effects.
- Own models end-to-end; from research through A/B experimentation to production calibration.
- Collaborate cross-functionally with ML engineers on pipeline and serving design, with data scientists on feature engineering, and with product and business stakeholders on spend strategy and ROI trade-offs.
- Actively coach and mentor less experienced team members, setting technical direction and providing guidance on causal modeling best practices.
Machine LearningCausal InferenceUplift ModelingSeniorHybridAmsterdamBooking.comSoftware DevelopmentResearchProduction ML