Machine Learning Scientist II - Pricing and Promotions

About Us: At Booking.com, data drives our decisions. Technology is at our core. And innovation is everywhere. Through our products, partners and people, we make it easier for everyone to experience the world.

About the Team

The Smart Value team at Booking.com develops Machine Learning models that optimize Pricing and Promotions across our global business units — including Accommodations, Car Rentals, Flights and more. We combine the science of Causal Inference, Uplift Modeling, Dynamic Pricing, and Optimization with large-scale data and experimentation to help the company make smarter decisions about how and when to adjust prices or run promotional campaigns.

We also build the Value Intelligence Platform, which provides the experimentation, analytics, and ML infrastructure that powers these decisions across the organization.

About the Role

As a Machine Learning Scientist in the Smart Value team, you will design, build, and deploy advanced models that guide pricing and promotional optimization across Booking.com. You will work closely with other scientists, engineers, analysts, and product teams to translate complex business challenges into scalable, data-driven solutions that deliver measurable impact.

You will:

• Develop and deploy models for causal inference, uplift estimation, and optimization to measure and maximize the incremental effect of price and promotion decisions.
• Design and improve dynamic pricing algorithms that balance competitiveness, conversion, and profitability.
• Contribute to the development of the Value Intelligence Platform, enhancing experimentation, simulation, and decision-support capabilities.
• Partner with product and business stakeholders to translate scientific insights into actionable strategies.
• Stay up to date with the latest advances in machine learning, causal modeling, and pricing optimization, and apply them pragmatically at scale.

Qualifications and Requirements

• Education and Experience
  • MSc or PhD (or equivalent experience) in a quantitative field such as Computer Science, Statistics, Economics, Operations Research, Mathematics, Engineering, Artificial Intelligence, or Physics.
  • Relevant professional or academic experience applying Machine Learning to business problems (typically MSc + 5 years, or PhD + 3 years).
  • Proven track record designing and executing end-to-end research and development projects, and generating measurable impact through large-scale ML model development. Evidence such as peer-reviewed publications, patents, or open-source contributions is a plus.
• Technical Expertise
  • Advanced knowledge and experience in Causal Inference, Uplift Modeling, Reinforcement Learning, Active Learning, and/or Optimization.
  • Strong proficiency in Python and modern ML frameworks (e.g., PyTorch, TensorFlow, XGBoost).
  • Experience working with large-scale data systems and production ML pipelines.
  • Solid understanding of data analytics, A/B testing, and statistical experimentation.
  • Experience with distributed computing and data technologies such as Spark, Hadoop, Kafka, and SQL.
  • Familiarity with version control systems and software engineering best practices.
• Collaboration and Communication
  • Experience collaborating cross-functionally with developers, analysts, product managers, and UX specialists to deliver machine learning–driven products.
  • Ability to communicate complex scientific and technical ideas clearly and effectively to both technical and non-technical audiences.
  • Excellent English communication skills, both written and verbal.
• Leadership and Impact
  • Demonstrated ability to drive technical, business, and process initiatives that improve productivity, model performance, and quality.
  • Lead by example — gaining respect through expertise and collaboration rather than hierarchy.
  • Ability to mentor and develop peers, provide timely feedback, and help teams achieve their goals.
  • Comfortable managing key performance indicators and aligning technical efforts with business objectives.

Benefits & Perks - Global Impact, Personal Relevance:

• Annual paid time off and generous paid leave scheme including: parental (22-weeks paid leave), grandparent, bereavement, and care leave
• Hybrid working including flexible working arrangements, working from home furniture and ergonomic support, and up to 20 days per year working from abroad (home country)
• A beautiful sustainable HQ Campus in Amsterdam, that offers on-site meals, coffee, and snacks, multi-faith and breastfeeding rooms at the office*
• Commuting allowance and bike reimbursement scheme
• Discounts & Wallet credits to spend on our products, upgrade to Booking.com Genius Level 3, and friends & family Booking.com discount vouchers
• Free access to online learning platforms, development and mentorship programs
• Global Employee Assistance Program, free Headspace membership

Pre-Employment Screening

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.