Data Scientist - Risk & Fraud

At Viber, we connect people–no matter who they are or where they are from. We dedicate ourselves to providing added value to communication — from private and group chats to audio and video calls to the ability to follow and contact brands, businesses, and celebrities. Viber is a super-app and an all-in-one platform for everything communications-based, with many different business and financial services, including payment solutions for users to make transfers, purchases, and much more!

As a Data Scientist, you will work in a highly collaborative environment with extensive amounts of data to research and develop deep learning models to solve real-world problems and apply them to key risk department initiatives such as predictive spam detection, performance forecasting, and identifying bad actors by using machine learning models.

Connecting people across the world is a complex problem with many machine-learning applications. The purpose of this role is to implement models and algorithms to solve complex business problems in risk & fraud domains. Successful outcomes will significantly impact our hundreds of millions of daily active users around the globe.

Requirements:

BSc in the field of engineering, math, statistics, etc.
Minimum of 2 years of experience in designing, developing and deploying production-level risk & fraud-related solutions with a proven business impact.
Worked in a team with peer review processes.
Fluency in Python and SQL.
Strong communication skills. Ability to present technical subjects to non-technical stakeholders.

Responsibilities:

Work with management and partner teams to design and implement solutions for given objectives.
Commitment to success metrics, ensuring low FP, and accurately measuring the impact and model performance
Lead technical efforts to improve the performance of our Risk & Fraud models and propose initiatives in that domain to shape our long-term risk-mitigation vision.
Autonomously find solutions to complex data problems and understand the data generation process and the challenges with the data.
Leverage the extensive data received from our application to enhance model performance and accuracy.

Advantages:

Knowledge in statistics: Statistical tests, Bayesian inference, MCMC, Likelihood estimators.
Led the efforts with a proven impact to mitigate Spam, Risk and Fraud at scale.
Strong passion for machine learning and investing independent time towards learning, researching and experimenting with new innovations in the field.
Experience working in AWS, NodeJS lambda functions, DataDog, and operational experience