PayPal Risk Fraud data-science solutions team in Tel-Aviv is seeking a highly motivated modeler to join our team.
The candidate will put practical work in big data, machine learning algorithms and PayPal business knowledge into building data science modeling solutions to solve some of most complex and challenging fraud problems in the fin-tech industry.
The ideal candidates are problem solvers, equipped with strong machine learning, analytical and programming skills suited to approach various kinds of challenges in complex environments. Candidates must be quick learners with a strong sense of personal responsibility and a high technical orientation.
Primary Job Responsibilities
- Build state-of-the-art statistical models, algorithms and procedures from start to end – solving a wide range of fraud problems while imporving customer experience.
- Extract and analyze large data volumes covering a broad range of information from user profile to transaction history.
- Research, develop and deploy solutions based on cutting-edge machine & deep learning algorithms.
- Communicate project status accurately and in a timely manner. Ensure that projects adhere to established standards and methodology practices.
- Deliver quality solutions consistently while working in a fast-paced, changing environment.
- M.Sc./ Ph.D. in the fields of Computer Science, Applied Mathematics, Statistics, or similar academic areas.
- 3+ year related experience for Master’s degree graduates or 1+ year experience for PhD graduates.
- Proficient in machine learning and data mining.
- Proficient code writing capability in a major programming language such as Python, R, Java and C++
- Experience with data mining leading packages – a plus.
- Excellent SQL hands on skills – a big plus.
- Experience in Big Data technologies such as Spark, Hadoop, Map Reduce processes - a big plus.
- Strong ability to coordinate and track multiple deliverables, tasks and dependencies.
- Excellent verbal and written communication skills on a broad set of technical topics.