Sr Staff Data Scientist II (Arch)- Viewer Experience

Key responsibilities:

• Lead the vision and strategy for recommendation algorithms across the JioStar platform, identifying opportunities to enhance personalization and content discovery
• Design and develop sophisticated recommendation models leveraging collaborative filtering, content-based techniques, deep learning, and hybrid approaches
• Translate complex business requirements into data science solutions, driving alignment across product, engineering, and business stakeholders
• Build evaluation frameworks and metrics that measure recommendation quality across dimensions including relevance, diversity, freshness, and business impactLead A/B testing and experimental design to validate algorithmic improvements and quantify business impact
• Develop novel approaches to recommendation challenges including cold-start problems, exploration-exploitation tradeoffs, and multi-objective optimization
• Collaborate closely with ML Engineering to ensure algorithms can be efficiently implemented at scale
• Analyze user behavior patterns to identify segments and personalization opportunities
• Provide mentorship to junior data scientists and establish best practices for the data science organization
• Stay current with research in recommendation systems and personalization, bringing innovative approaches to our platform

Skills and attributes for success:

• Deep expertise in recommendation system algorithms, including collaborative filtering, content-based, neural networks, and multi-stage approaches
• Experience with candidate generation, ranking, and slate optimization for personalized user experiences
• Strong background in reinforcement learning, bandits, and long-term reward modeling for recommendation systems
• Experience with transformer architectures, LLMs, and their application to personalization
• Knowledge of RLHF reward modeling/alignment techniques for improved recommendation systems
• Hands-on experience with Python, SQL, and TensorFlow/PyTorch for implementing and evaluating algorithms
• Knowledge of multi-task learning, transfer learning, and embedding techniques for users, items, and contexts
• Understanding of content life cycles, seasonality, and timing's impact on recommendation strategies
• Proven track record of developing recommendation systems that drive meaningful business outcomes
• Experience with experimental design and A/B testing methodologies for recommendation algorithms
• Ability to balance algorithmic exploration with user enjoyment in recommendation design
• Strong leadership capabilities with demonstrated experience mentoring junior data scientists

Preferred education and experience:

• Master's or PhD in Computer Science, Statistics, Mathematics, or related quantitative field with 10+ years of experience in applied data science, including at least 5 years working specifically with recommendation systems. Experience in streaming media, entertainment, or similar content platforms strongly preferred.