Analyst within Credit Risk Management team identifying credit segmentation opportunities using statistical methods. Collaborating with teams to enhance credit decision process and policies.
About the role
- 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
Requirements
- 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.
- 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
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