Senior Machine Learning Scientist - Accommodations

Booking.com; Amsterdam; Hybrid; EUR 110,000 - 180,000; Full time

Benefits offered by this job

Annual leave and leave schemes

Hybrid work with abroad days (up to 20

Job summary

Booking.com is seeking a Senior Machine Learning Scientist to design, build, and deploy uplift and causal inference systems for the accommodation marketplace. You will run large-scale A/B experiments, optimize promotional spend, and advance neural architectures for tabular data.

You will collaborate with ML engineers, data scientists, and product stakeholders to deliver ML-powered products while mentoring peers and publishing when appropriate.

Qualifications

• Advanced knowledge of causal inference and uplift modeling.
• Experience designing end-to-end R&D plans with measurable impact.

Responsibilities

• Design and deploy uplift models estimating heterogeneous treatment effects.
• Develop causal inference methodologies including IPW and sensitivity analyses.
• Advance neural network architectures for uplift on tabular data with attention and embeddings.
• Collaborate with ML engineers on data pipelines and serving architectures.
• Mentor junior team members and set technical directions.

Skills

Causal Inference

Uplift Modeling

Python

Experiment Design

Communication

Education

MSc/PhD in Quantitative Field

Tools

Spark

Airflow

TensorFlow

PyTorch

LightGBM

XGBoost

Job description

About Us

At Booking.com, data drives our decisions. Technology is at our core. And innovation is everywhere. But our company is more than datasets, lines of code or A/B tests. We’re the thrill of the first night in a new place. The excitement of the next morning. The friends you make. The journeys you take. The sights you see. And the food you sample. Through our products, partners and people, we make it easier for everyone to experience the world.

About The Team

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.

Role Description

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.

Key Job 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.

Qualifications & Skills

• MSc or PhD (or equivalent experience) in a quantitative field such as Computer Science, Statistics, Economics, Econometrics, Operations Research, Mathematics, or Physics.
• Relevant professional or academic experience applying Machine Learning to business problems (typically MSc + 4 years, or PhD + 2 years).
• 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.
• 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.
• Strong proficiency in Python and modern ML frameworks (e.g., TensorFlow, PyTorch, LightGBM, XGBoost).
• Solid understanding of experimental design, A/B testing, and statistical methodology — including awareness of SUTVA violations, selection bias, and observational study limitations.
• Experience working with large-scale data systems and production ML pipelines (Spark, Airflow, or similar).
• Experience with neural network design for structured/tabular data (embeddings, attention, multi-task architectures) is a strong plus.
• Experience collaborating cross-functionally with developers, analysts, product managers, and other scientists to deliver ML-powered products.
• 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.

Benefits & Perks

• Annual paid time off and generous paid leave scheme including parent, grandparent, bereavement, and care leave.
• Hybrid working including flexible working arrangements, and up to 20 days per year working from abroad (home country).
• Industry leading product discounts – up to 1400 per year – for yourself, including automatic Genius Level 3 status and Booking.com wallet credit.

Inclusion at Booking.com

Inclusion has been a core part of our company culture since day one. This ongoing journey starts with our very own employees, who represent over 140 nationalities and a wide range of ethnic and social backgrounds, genders and sexual orientations. We ensure that colleagues with disabilities are provided the adjustments and tools they need to participate in the job application and interview process, to perform crucial job functions, and to receive other benefits and privileges of employment.

Application Process

• This role does not come with relocation assistance.

Booking.com is proud to be an equal opportunity workplace and is an affirmative action employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, or veteran status. We strive to move well beyond traditional equal opportunity and work to create an environment that allows everyone to thrive.

If your application is successful, your personal data may be used for a pre-employment screening check by a third party as permitted by applicable law. Depending on the vacancy and applicable law, a pre-employment screening may include employment history, education and other information (such as media information) that may be necessary for determining your qualifications and suitability for the position.