Senior Data Scientist (RecSys)

About Pocket FM

Pocket FM is the world's largest audio-series platform, where powerful stories travel effortlessly across languages, cultures, and continents. From India and the US to Europe and beyond, we are redefining how stories are created, discovered, and experienced across the globe, without screens.

At our core, we are an AI-powered entertainment company with a deeply human-first philosophy. We believe technology should amplify creativity. Our proprietary AI systems work alongside writers, voice artists, and creative teams to help stories scale globally, faster, smarter, and with cultural depth and emotional integrity intact.

Today, Pocket FM is home to a vibrant community of 250+ million listeners, 300,000+ creators, and 100,000+ audio series. With over 140 billion minutes streamed annually, we have emerged as one of the fastest-growing media-tech companies in the world, and we are just getting started. We operate at a massive global scale, but with a startup mindset: curious, fast-moving, and deeply owner-driven. At Pocket FM, teams are encouraged to think boldly, move with intent, and build for long-term impact as we shape the future of audio-first storytelling worldwide

About the Role

As a Principal Data Scientist, you'll lead the development of intelligent ML solutions that drive personalization, content discovery, and user engagement. Your insights will be instrumental in enhancing user satisfaction and driving overall business success.

What you'll do

• Lead the design and development of scalable machine learning solutions for recommendations, content intelligence, and user personalization
• Build predictive models to forecast content performance, user engagement, retention, and long-term user value, enabling data-driven content and product decisions
• Drive innovation in recommendation systems, including ranking, candidate generation, personalization, exploration-exploitation strategies, and reinforcement learning where applicable
• Partner closely with Product, Content, Engineering, and Marketing teams to optimize content discovery, release strategies, and user experiences through data-driven insights
• Design, execute, and interpret large-scale online experiments (A/B testing) to evaluate product and recommendation improvements
• Analyze user behavior and content performance to uncover actionable insights that influence product strategy and roadmap
• Develop robust evaluation frameworks, metrics, and monitoring systems to continuously improve model quality and business impact
• Champion ML best practices, and raise the technical bar across the team
• Stay at the forefront of advancements in machine learning, recommender systems, LLMs, and applied AI, translating research into production-ready solutions

What are we looking for

• 3+ years of experience building and deploying machine learning solutions, with significant experience in recommendation systems, personalization, content intelligence, or user behavior modeling
• Strong foundation in machine learning algorithms, statistical modeling, and experimentation methodologies
• Expertise in recommendation systems, including learning-to-rank, candidate retrieval, personalization, exploration vs. exploitation, offline and online evaluation, and A/B testing
• Experience building production-grade ML models using frameworks such as PyTorch, TensorFlow, Scikit-learn, XGBoost, or similar
• For content intelligence use cases, experience applying LLMs, NLP, embeddings, and modern AI techniques to content understanding and optimization is highly preferred
• Strong programming skills in Python and experience working with SQL and distributed data processing frameworks such as Spark or Hadoop
• Proven ability to translate complex analytical findings into business impact and influence cross-functional stakeholders
• Strong product mindset with the ability to balance technical excellence with user and business outcomes
• Passion for creating exceptional user experiences through intelligent, data-driven products