Senior Data Scientist – Predictive Modeling/Machine Learning – Pocket FM

About Pocket FM

Pocket FM is at the forefront of audio entertainment innovation in India and beyond. We are pioneering the creation of the audio OTT landscape and curating the largest collection of audio streaming content, including audio series, audiobooks, and podcasts, through our unique storytelling approach.

Our mission is to empower communities to share stories and knowledge by reimagining traditional audio patterns and building an internet-scale platform. With 80 million monthly active listeners spending over 110 minutes daily, and total monthly audio streaming exceeding 4 billion minutes, Pocket FM is a Series C funded startup backed by prestigious investors like Naver, Goodwater Capital, Lightspeed, Tanglin Venture Partners, and others.


We are currently seeking experienced Data Scientists to join our team across multiple domains. As a Data Scientist, you will play a vital role in optimizing our recommendation systems and content strategy to enhance user retention and engagement. Leveraging your expertise in machine learning, recommender systems, data analysis, and content understanding, you will determine the ideal content to launch, timing, format, and recommendations to drive user satisfaction and overall business success.

Your insights will be instrumental in shaping user experiences and impacting our platform’s growth. Collaborating closely with backend, content, product, and engineering teams, you will drive impactful initiatives. Some of the specific areas you may focus on include:

Recommendation Systems: Enhancing the homepage and autoplay experience using advanced recommendation techniques tailored to the Pocket FM platform.

Content Analysis: Employing advanced data analysis and machine learning algorithms to analyze content performance and identify patterns for higher retention and engagement.

Predictive Modeling: Developing predictive models to forecast the impact of new content releases on user behavior and satisfaction.

Content Optimization: Collaborating with content creators and product teams to optimize content release strategies for maximum engagement.

A/B Testing: Planning, designing, and analyzing A/B tests to evaluate content variations and make data-driven decisions.

Stay updated with the latest advancements in recommender systems, machine learning, and content analytics, proposing innovative approaches to improve content strategies.


Education: Master’s degree in Computer Science, Data Science, Statistics, Mathematics, or related field (Ph.D. is a plus).

Experience: Minimum 3 years of experience in data science with expertise in recommendation systems, content analysis, user behavior modeling, and predictive analytics.

Machine Learning Expertise: Strong knowledge of recommendation systems, learning algorithms, reinforcement learning, and A/B testing. Proficiency in traditional ML methods and deep learning frameworks like TensorFlow and PyTorch.

Programming Skills: Proficient in Python, R, or similar languages for data analysis and modeling.

Big Data Tools: Familiarity with tools like Hadoop, Spark, SQL is advantageous.

Communication Skills: Excellent communication and presentation skills to convey complex insights to technical and non-technical stakeholders.

Analytical Thinking: Strong problem-solving skills with attention to detail.

Team Player: Ability to work collaboratively in a team environment.

Passion for Content and User Experience: Genuine interest in content creation and user engagement.

Preferred Additional Skills:

Experience in entertainment, media, or digital content industry.

Familiarity with natural language processing (NLP) techniques.

Knowledge of cloud-based data platforms like AWS, Google Cloud, or Azure.

Join us in shaping an exceptional content experience for our users. If you’re passionate about recommender systems, content analytics, and machine learning, apply now and become part of our journey towards content excellence!

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