Key Job Responsibilities and Duties:
- Data pre-processing and analysis: Collaborate with data engineers and machine learning engineers to collect, clean, pre-process, and transform large and wide datasets for model features and data monitoring. Conduct exploratory data analysis (EDA) to uncover insights and identify patterns that boost the model performance.
- Model Building: Utilize the data to design, train and build advanced recommendation models for different disciplines in Booking combining neural networks, LLMs and other SOTA technologies.
- Model evaluation and optimization: Conduct detailed model evaluation metrics and validation to ensure accuracy, reliability, and scalability. Optimize model performance by fine-tuning hyper parameters, feature engineering, and applying techniques such as ensemble learning and continuous learning.
- Deployment and integration: Work closely with software engineers to integrate machine learning models into production systems. Ensure seamless deployment and efficient model inference in real-time environments.
- Collaborate with multidisciplinary teams: Collaborate with product managers, data scientists, and analysts from other teams to understand business requirements and translate them into machine learning solutions. Provide technical guidance and mentorship to junior team members.
We have found that people who match the following requirements are the ones who fit us best:
- Masters, or equivalent experience in a quantitative field (Computer Science, Mathematics, Engineering, Artificial Intelligence, etc.)
- Minimum of 5 years of experience as a Machine Learning Scientist or a similar role, with a consistent record of successfully delivering ML solutions.
- Practical experience with RecSys algorithms & methods, delivering recommendation/Ranking models to production, preferably in a large scale multi-sided marketplace
- Strong programming skills in languages such as Python.
- Experience with cloud frameworks like AWS sagemaker and training models using TensorFlow, PyTorch, or scikit-learn.
- Experience with data at scale using MySQL, Pyspark, Snowflake and similar frameworks.
- Proficiency in data manipulation, analysis, and visualization using tools like NumPy, pandas, and matplotlib.
- Experience with experimental design, A/B testing, and evaluation metrics for ML models.
- Experience of working on products that impact a large customer base is an advantage
- Excellent communication in English; written and spoken
Booking.com’s Total Rewards Philosophy is not only about compensation but also about benefits. Our Total Rewards strive to make it easier for you to experience all that life has to offer on your terms so that you can focus on what really matters. We offer competitive compensation as well as thoughtful, valuable, and even fun benefits which include:
- Headquarters located in one of the most dynamic and cosmopolitan cities in Europe: Amsterdam.
- Contribute to a high scale, sophisticated, world-class product and see the real time impact of your work on millions of travelers worldwide.
- Be part of a truly international fast paced environment and performance driven culture.
- Performance-based company that offers career advancement, and lucrative compensation, including bonuses and stock potential.
- Discount on Booking.com accommodations with the “Booking Deal” including other perks and benefits.
- Company-sponsored family and social activities to help our employees become integrated with each other and the company's culture.
- Diverse and creative colleagues from every corner of the world.
- Health, life, and disability insurance*
- 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 for yourself, friends, and family, including automatic Genius Level 3 status and quarterly Booking.com wallet credit.
- Free access to online learning platforms, development and mentorship programs, and a complimentary 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.
