Staff Data Scientist - Viewer experience

BengaluruFull Timelead

OtherStaff Data Scientist

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Job Summary: Lead the development of sophisticated recommendation algorithms that power personalized content discovery across JioStar's platform, driving measurable improvements in user engagement,

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OtherStaff Data Scientist

Job Summary: Lead the development of sophisticated recommendation algorithms that power personalized content discovery across JioStar's platform, driving measurable improvements in user engagement, satisfaction, and retention through data-driven innovations.

About the team:

The Viewer experience team focuses on revolutionizing content discovery across JioStar's platform. Our aim is to provide users with an intuitive and personalized experience, enabling seamless exploration and enrichment of their interests. Our data science team develops advanced algorithms that understand user preferences and content relationships to deliver relevant recommendations at scale.

Working alongside ML engineers, developers, designers, and content curators, we translate business challenges into algorithmic solutions that analyze user preferences and behaviors to deliver immersive content discovery experiences. Our work directly influences how millions of users discover content daily, making our team a critical driver of user engagement and business success. We embrace a culture of experimentation, rigorous evaluation, and continuous improvement to enhance our recommendation capabilities.

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 impact

Lead 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

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

Bachelor's 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.