Data Scientist in the fraud domain are highly motivated team players who specialize in building scalable system that identify fraud patterns. The Data Scientist overcome challenges presented by big data, evolving fraud techniques and new payment technologies by leveraging area expertise, data science, advanced classification algorithms and domain knowledge. They follow those mechanisms development process from start to finish, and work closely with technologists and developers to bring their analytical insights to life.
The ideal candidates are problem solvers, equipped with strong analytical skills suited to approach various kinds of challenges in complex environments. Adept at creative and critical thinking, they are able to deconstruct the problem and transform personal insights into large scale, state-of-the-art solutions. Candidates must be quick learners with a strong sense of personal responsibility and technical orientation.
Requirements:
2+ year of experience solid understanding of probability, statistics, machine learning, data science, A/B testing & analysis of ML models.
End-to-end system design: data analysis, feature engineering, technique selection & implementation, debugging, and maintenance in production.
Optimizing models for accuracy and performance.
Experience with Hadoop, Spark, or other distributed computing systems for large-scale training & prediction with ML models.
Hands on with SQL, to be able to pull data from various upstream sources and transform it.
Hands on with at least one scripting language like Python/R etc.
Quick-thinker, fast learner, wide general knowledge, problem solver
Team worker, responsible , delivery-oriented
Excellent spoken and written English
PayPal Holdings, Inc. is an American company operating a worldwide online payments system. Online money transfers serve as electronic alternatives to traditional paper methods like checks and money orders