Senior Applied Scientist

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 Senior Applied 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 tasks such as forecasting, recommendation systems, anomaly detection, and analytics at a high scale.

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

Responsibilities
Work with management and partner teams to design and implement key results for given objectives
Lead technical efforts to improve the performance of deployed models and propose initiatives to shape the long-term scientific vision
Autonomously find solutions to complex data science problems, with potentially non-trivial solutions and understand the data generation process and challenges with the data
Leverage the extensive data received from our application to enhance model performance and accuracy

Requirements
Master’s degree in Statistics, Mathematics or Computer Science
Minimum of 4 years of experience in designing, developing and deploying production-level deep learning models with a proven business impact
Fluency in Python, Pandas/Dask, SQL, PyTorch or Tensorflow. Ability to write readable and maintainable code
Strong communication and storytelling skills with both technical and non-technical audiences. Ability to present complex technical subjects to non-technical stakeholders
Ability to read AI research publications and implement the algorithms & architectures from scratch

Advantages
Advanced knowledge in statistics: Statistical tests, Bayesian inference, MCMC, Likelihood estimators, etc.
Publication in peer-reviewed conferences or journals on reinforcement learning, deep learning, and machine learning
Strong passion for machine learning and investing independent time towards learning, researching and experimenting with new innovations in the field
Experience working with technologies like SageMaker, Athena/Trino, Spark, Milvus, and OpenSearch