Data Scientist- Recommendation Systems

Rakuten Viber is one of the most popular and downloaded apps in the world. Working with us provides a unique opportunity to influence hundreds of millions of our users and to be part of the journey that makes us a super-app. Our mission is to make people’s lives easier by enabling meaningful connections, from precious moments with family and friends, through managing business relationships to pursuing their passions.

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 recommendations and classification. Successful outcomes will significantly impact our hundreds of millions of daily active users around the globe.

As a Data Scientist, you will work in a highly collaborative environment with extensive amounts of data to research and develop deep learning models in the domains of dating, moderation and content segmentation and apply them to tasks such as recommendation systems and analytics at a high scale.

Responsibilities

• Work with management and partner teams to design and implement solutions in recommender systems for given objectives.
• Lead technical efforts to improve the performance of deep learning models and propose initiatives to impact company goals directly.
• Autonomously find solutions to complex problems in social network recommendations and understand the data generation process and challenges with the data.
• Analyze and 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 recommendation 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 generative models: Auto-encoding, adversarial models, compression.
• Worked on deep learning graph model solutions with 10’s of TB of data.
• 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.