Where: Tel Aviv (Israel)
Extent of Work: Full Time
Click here to apply in English
- Work in a multi-disciplined team where you take ownership of the modelling part of turning data into customer experience improvements. This means using data to generate or validate hypotheses about improvement opportunities, building the models that make these improvements happen and following up on feedback generated by millions of customers.
- Use the autonomy teams have and test hypotheses early and often on suitable parts of our traffic.
- Actively contribute to taking Data Science at Booking.com to the next level
B.skilled – Required
- Minimum three years of relevant work experience
- Masters, PhD, or equivalent experience in a quantitative field (Computer
- Science, Mathematics, Engineering, Artificial Intelligence, etc.)
- Experience with at least one scripting language, e.g. R, Octave, and one programming language, e.g. Python, Perl, C/C++, Java
- Exposure to SQL and relational databases
- The ability to train elementary machine learning models, to select the right tool or the right task and to judge when a model is good enough for a particular purpose
- An all-around data scientist: strong statistics background, experience in algorithm programming, experimentation, data visualization, machine learning, optimization and big data
- Excellent communication skills; the ability to convey complex analysis results clearly and with conviction to all stakeholder levels
B.skilled – Preferred
- Experience implementing a real-world Recommender or Ranking system
- Worked with the Hadoop Ecosystem
- Implemented multi-core/distributed software, preferably in a Linux environment
- Experience solving real problems using machine learning techniques and with statistical rigor
- Prior practical experience with the elementary machine learning models such as logistic regression and random forests, and experience with one more other areas such as: (Deep) Neural Networks, Natural language processing Hidden Markov Models, Conditional Random Fields, Game Theory, Mechanism Design, Latent Dirichlet Allocation.