Senior Machine Learning Scientist (Accommodations)
Confirmed on the employer's own hiring board on Oct 10, 2026. First seen by Alion on Sep 23, 2026.
Join Booking.com as a Senior Machine Learning Scientist in the Accommodation Business Unit (ABU). You will design, build, and deploy uplift models and causal inference systems that optimize promotional spend across our accommodation marketplace. This role combines causal methodology, neural network architecture design, and production ML, with your work validated through large-scale A/B experiments. You will also have opportunities to publish applied research at top venues.
Missions
- Design, build, and deploy uplift models and causal inference systems that allocate promotional spend across Booking.com’s accommodation marketplace.
- Advance the team’s neural network architectures for uplift modeling on tabular data, balancing model expressiveness with production latency requirements.
- 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.
Profil recherché
- Solid understanding of experimental design, A/B testing, and statistical methodology - including awareness of SUTVA violations, selection bias, and observational study limitations- Experience collaborating cross-functionally with developers, analysts, product managers, and other scientists to deliver ML-powered products
- Strong proficiency in Python and modern ML frameworks (e.g., TensorFlow, PyTorch, LightGBM, XGBoost)
- Proven track record designing and executing end-to-end R&D plans, and generating measurable impact through large-scale ML model development. Evidence such as peer-reviewed publications, patents, or open-source contributions is a plus
- Relevant professional or academic experience applying Machine Learning to business problems (typically MSc + 4 years, or PhD + 2 years)
- Experience with neural network design for structured/tabular data (embeddings, attention, multi-task architectures) is a strong plus
- MSc or PhD (or equivalent experience) in a quantitative field such as Computer Science, Statistics, Economics, Econometrics, Operations Research, Mathematics, or Physics
- Advanced knowledge and experience in Causal Inference, Uplift Modeling, or Treatment Effect Estimation. Experience with heterogeneous treatment effects, interference / spillover effects, or policy learning is highly valued
- Experience working with large-scale data systems and production ML pipelines (Spark, Airflow, or similar)
- Excellent English communication skills, both written and verbal. Ability to communicate complex causal reasoning clearly to both technical and non-technical audiences
- Successfully driving technical initiatives and cross-team collaboration while communicating with stakeholders at all levels