Senior Machine Learning Scientist (Accommodations)

• This opening is for the ABU ML team within Margin Management of the Accommodation Business Unit (ABU)
• The ABU ML team develops causal machine learning systems that power one of Booking.com’s biggest customer acquisition channels
• We predict which promotional investments will drive genuinely incremental demand and deploy these models in production at scale
• The work involves uplift modeling, causal inference under marketplace interference, neural network design for structured data, and rigorous online experimentation
• The team actively contributes to the research community, our recent work “Converted Data is All You Need for Causal Optimization of e-Commerce Promotions” was published at ACM CIKM 2025
• We encourage publishing and conference participation when the work advances the state of the art
• As a Senior Machine Learning Scientist, you will design, build, and deploy uplift models and causal inference systems that allocate promotional spend across Booking.com’s accommodation marketplace
• The role combines causal methodology, neural network architecture design, and production ML — with your work validated through large-scale A/B experiments
• There are opportunities to publish applied research at top venues when the work contributes novel methodology
• 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

Benefits

• Health insurance
• Free access to Headspace for you and your loved ones
• Global Employee Assistance Program
• Meditation and Breastfeeding rooms at the office
• Booking Cares - 2 days per year to volunteer and learn
• Life insurance
• Disability insurance
• Pension plan
• Annual paid time off
• Parental leave - 22 weeks
• Grandparent leave - 10 days
• Care leave - 10 days
• Bereavement leave - up to 4 weeks
• Anniversary leave
• Working from Home Furniture and Ergonomic Support
• Working from Abroad - up to 20 days per year
• Discounts & Wallet credits to spend on our products
• Upgrade to Booking.com Genius Level 3
• Friends & Family Booking.com discount vouchers
• Free access to online learning platforms
• Development and mentorship programs to support career growth
• Access to trainings and workshops
• Team development opportunities
• Local discount programs
• Game rooms in offices
• On-site meals, coffee and snacks including vegan options- 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