Staff Data Scientist

About Pocket FM

Pocket Entertainment is revolutionizing entertainment through immersive storytelling and AI-powered personalization. With millions of users worldwide and billions of minutes consumed every month, we are building the future of entertainment by combining human creativity with cutting-edge AI technologies.

At Pocket FM, you will work on category-defining AI products that shape content discovery, recommendation systems, creator intelligence, and user engagement at massive scale while collaborating with world-class engineers, AI researchers, product leaders, and creators.

Role Overview

We are looking for an experienced Staff Data Scientist to drive high-impact machine learning and analytics initiatives across recommendation systems, personalization, user engagement, content intelligence, and growth optimization.

In this role, you will build scalable ML systems, influence product strategy, uncover deep user behavior insights, and help shape the future of AI-driven entertainment experiences. Depending on experience and level, you will also mentor teams, lead cross-functional initiatives, define technical direction, and contribute to long-term ML strategy and platform evolution.

This role is highly hands-on and ideal for someone who combines strong machine learning expertise, product thinking, experimentation rigor, and business impact orientation.

Key Responsibilities

Machine Learning & Personalization

• Design, build, and deploy scalable machine learning models for: Recommendations, Ranking and search, Personalization, Churn prediction, User lifecycle modeling
• Retention and monetization optimization
• Creator/content intelligence
• Improve recommendation quality and personalization strategies using user interaction and content consumption signals.
• Develop predictive models to forecast user behavior, content performance, engagement, and satisfaction.

Product & Business Impact

• Partner closely with product, engineering, growth, business, and content teams to identify high-impact opportunities and translate business problems into scalable ML solutions.
• Drive initiatives across engagement, retention, content discovery, growth, and platform optimization.
• Translate large-scale user behavior and content consumption data into actionable product and business insights.
• Influence roadmap decisions through deep analytical rigor and experimentation.

Experimentation & Analytics

• Conduct exploratory data analysis to identify trends, opportunities, and product improvements.
• Design, execute, and evaluate experiments including A/B tests and causal inference frameworks.
• Establish best practices for experimentation, statistical inference, metrics design, and model evaluation.

End-to-End ML Ownership

• Own the complete ML lifecycle: Data preparation, Feature engineering, Model training, Evaluation, Deployment, Monitoring
• Governance and observability
• Collaborate with ML engineers and platform teams to productionize and scale ML systems
• Drive MLOps best practices including model versioning, monitoring, scalability, and lifecycle management

Leadership & Mentorship

• Mentor junior and senior data scientists through technical guidance, code reviews, and best practices
• Contribute to technical strategy, architecture decisions, and long-term ML vision
• Raise the technical bar across the organization and foster a culture of innovation, experimentation, and continuous learning
• Lead cross-functional initiatives and influence stakeholders across product and engineering organizations

Innovation & Research

• Stay updated with advancements in:
• Recommender systems
• NLP and LLMs
• Generative AI
• Reinforcement learning
• Content intelligence
• Evaluate and integrate emerging AI technologies into consumer-facing experiences

Qualifications Required

• Bachelor's, Master's, or PhD in Computer Science, Machine Learning, Statistics, Mathematics, or a related quantitative field
• 5–10+ years of experience building and deploying machine learning systems in production environments (level dependent)
• Strong expertise in: Recommendation systems, Ranking and personalization, User behavior modeling, Predictive analytics, Experimentation and statistical inference
• Strong programming skills in Python
• Hands-on experience with ML frameworks and tools such as: PyTorch, TensorFlow, Scikit-learn, XGBoost
• Experience with large-scale data processing systems such as: Spark, Databricks, Kafka, Hadoop, SQL
• Experience working with cloud platforms such as AWS or GCP
• Strong analytical thinking, problem-solving, and communication skills
• Ability to work effectively with cross-functional stakeholders and influence product direction.

Preferred Qualifications

• Experience in consumer internet, media-tech, gaming, entertainment, or digital content platforms
• Familiarity with: NLP, LLMs, Generative AI, Multimodal AI systems, Reinforcement learning
• Experience working on high-scale recommendation, ranking, or personalization systems
• Research publications, patents, open-source contributions, or technical blogs in ML/AI
• Passion for storytelling, content intelligence, and user experience innovation.