Machine Learning Scientist

Company Description

Job Description

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.

The Tel Aviv Machine Learning Center is looking for data savvy professionals to join our team of data scientists. You will have the opportunity to build data products that will be used across various departments of our business to help us predict and understand our customers’ behaviour, enabling us to offer them the most relevant personalised experience.

Machine learning projects at the center cover topics such as recommendation systems, computer vision, natural language understanding, reinforcement learning and more.

As a Machine Learning Scientist, you’ll have the chance to work on exciting products and play an integral role in taking it to the next level!  You’ll have the chance to collaborate closely with other developers, scientists, engineers, product managers, designers and various business stakeholders and take full ownership of your work - from the initial idea generation phase to the implementation of the final product.

You will thrive at Booking.com if you are result-focused, innovative, and with a solid quantitative background, but most of all by understanding our complex business model and the logic behind it.

• Work in a multi-disciplined team where you’ll take full ownership of turning discoveries and ideas into products through machine learning (incl. understanding product requirements, data discovery, model development and evaluation, to implementation of a full production pipeline for both batch and stream-based deployment).
• Use the model’s output to deliver both short-term commercial impact and longer-term differentiated business value and customer experience.
• Define and build proof-of-concepts to test new ideas and demonstrate their potential value to relevant stakeholders. 
• Document and share the findings, and identify when these technologies can be generalised into reusable frameworks.
• Continuously evolve your craft by keeping up to date with the latest developments in ML/AI and related technologies, and upskilling on these as needed.
• Actively contribute to Machine Learning at Booking.com through training, exploration of new technologies, interviewing, onboarding and mentoring colleagues.

• At least 3 years of relevant work experience.
• Masters, PhD, or equivalent experience in a quantitative field (Computer Science, Mathematics, Engineering, Artificial Intelligence, etc.)
• Prior experience with classification and regression algorithms and an excellent understanding of the specifics of those algorithms.  Experience with deep learning is a plus.
• Solid understanding of fundamental machine learning concepts. 
• Fluency in at least one programming language, with a strong preference for Python.
• Strong working knowledge of Spark and SQL.
• Experience with putting machine learning models in production is a plus.
• Excellent English communication skills, both written and verbal; the ability to convey your message to team members and other stakeholders.

B.Offered

• 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
• Diverse, unique colleagues from every corner of the world

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, colour, 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.