Job description

Description Tango is a successful, market leader, a live-streaming Platform with 450+ million registered users, in an industry projected to reach $240 BILLION in the next couple of years. The B2C platform, based on the best-quality global video technology, allows millions of talented people around the world to create their own live content, engage with their fans, and monetize their talents. Tango live stream was founded in 2018 and is powered by 500+ global employees operating in a culture of growth, learning, and success! The Tango team is a vigorous cocktail of hard workers, creative brains, energizers, geeks, overachievers, athletes, and more. We push the limits to bring our app from “one of the top” to “the leader”. The best way to describe Tango's work style is not to use the word “impossible”. We believe that success is a thorny path that runs on sleepless nights, corporate parties, tough releases, and, of course, our users' smiles (and as we are a LIVE app, we truly get to see our users all around the world smiling right in front of us in real-time!). Do you want to join the party? Responsibilities Design, implement, and improve ML and AI models to drive business outcomes across multiple domains, such as recommendation systems, image recognition, ChatBot, etc. Own the end-to-end ML lifecycle: data preprocessing, feature engineering, model training, validation, and deployment. Expert level in designing evaluation pipelines to prove performance, scalability, and consistency Leverage generative AI and LLMs to enhance existing workflows and explore new product opportunities. Collaborate with Product, Engineering, and Analytics teams to align modeling efforts with business needs. Clearly communicate complex findings and model insights to stakeholders. Requirements BSc or higher in Computer Science, Mathematics, Statistics, or related fields. 4+ years of hands-on experience as a Data Scientist, ideally within mobile, gaming, or social network industries. Proven experience with AI/ML frameworks and toolkits (Scikit-learn, TensorFlow, PyTorch, LangChain, etc.) Familiarity with MLOps best practices, model versioning, experiment tracking, and continuous deployment. Strong knowledge of machine learning techniques: Classification, regression, segmentation, reranking, model interpretability Solid background in data analysis and statistics; ability to design experiments and interpret results. Experience working in cloud environments, especially Google Cloud Platform (GCP) — BigQuery, GCS, Vertex AI (a plus). Comfortable working in fast-paced, production-critical environments with a sense of ownership and accountability. Advantages: Experience developing recommendation engines, especially with deep learning and reranking techniques. Experience with LLM-based applications, prompt engineering, Agents development, etc. #LI-SP1