Senior Data Scientist

📋 Description

• • Own the end-to-end lifecycle of advanced statistical, AI and machine-learning models that power a hyper-personalized content-discovery experience for 750 million weekly viewers. Your models will decide—in milliseconds—whether a user sees a nail-biting IPL finish, the latest K-drama episode, or a long-tail documentary, directly influencing watch-time, retention and lifetime value.
• • Architect forecasting systems that predict content appetite across genres ranging from live sports to VOD originals. Blend historical consumption signals, real-time viewing context, social buzz and external events to anticipate demand spikes and troughs, enabling content-ops teams to pre-emptively cache, promote or retire assets.
• • Design next-generation recommendation engines that move beyond simple CTR or watch-time metrics. Build state-machine models that optimize for complex funnel outcomes—activation, free-to-paid conversion, cross-device journey completion and long-term LTV—while respecting business constraints such as ad-load, subscription tiers and regional licensing.
• • Integrate models into low-latency, high-throughput production pipelines serving 100K+ requests per second. Partner with Data Engineering to implement feature stores, model registries, A/B testing gates and real-time feature computation on Spark, Flink and cloud-native stacks (AWS/GCP).
• • Lead experimentation at scale: craft hypothesis trees, design multi-armed bandits and quasi-experiments, and build self-service dashboards that allow product and marketing squads to launch, monitor and interpret tests without hand-offs.
• • Champion data quality and governance. Establish automated anomaly detection, drift monitoring and fairness audits so that every model refresh meets statistical, regulatory and ethical standards across diverse Indian and global audiences.
• • Translate complex findings into crisp narratives for C-suite, content commissioners and ad-sales leadership. Deliver quarterly “state-of-the-viewer” briefings that shape billion-rupee content investments and ad-inventory pricing strategies.
• • Stay on the bleeding edge. Run internal R&D sprints on LLMs, diffusion models and causal-inference techniques; publish at top-tier conferences and feed learnings back into production systems.
• • Mentor a growing pod of data scientists and ML engineers. Codify best practices in MLOps, code reviews and storytelling, raising the bar for reproducibility and impact measurement across the Central Analytics matrix.
• • Collaborate with Ad Tech teams to monetize insights without compromising viewer trust. Build privacy-safe targeting models that balance revenue lift with user satisfaction, ensuring compliance with GDPR, India DPDP and emerging regulations.