Research Scientist – Recommendation, Content Science
2 weeks ago
About Pocket FM
It’s an amazing time to join Pocket FM as we continue shaping the future of entertainment globally with audio series. With a strong repository of audio series, Pocket FM has evolved as the powerhouse of entertainment.
Our mission is to give wings and wisdom to stories, transforming them into a strong entertainment package. Pocket FM has grown to 130 million listeners community, spending an average user time of over 115 minutes every day, while the total streaming on the app counts to over 6.5 billion minutes monthly.
Summary
We are seeking skilled and experienced Research Scientists to join our team in multiple areas. As a Research Scientist, you will play a crucial role in developing state-of-the-art methods for our recommendation systems and content strategy for higher user retention and engagement. You will leverage your expertise in machine learning, recommender systems, data analysis, and content understanding to determine the ideal content to launch, when to launch it, and what format it should take, when to recommend and where to recommend etc. Your insights will be instrumental in enhancing user satisfaction and driving overall business success. You will work very closely with the back-end teams, content teams, product and engineering teams and will be responsible for driving impact. Some of the sub-areas that you could be working on are listed below:
Recommendation Systems: Improve the homepage user experience, autoplay experience using state-of-the-art recommendation techniques and by discovering problems specific to the PocketFM platform and solving them.
Content Analysis: Utilize advanced data analysis techniques and machine learning algorithms to analyze the performance of existing content and identify trends and patterns that contribute to higher retention and engagement rates.
Predictive Modeling: Develop predictive models that can accurately forecast the potential impact of new content releases on user behavior, retention, and satisfaction and use them for content and recommendation use cases.
Content Optimization: Collaborate with content creators, product managers, and marketing teams to optimize the content release strategy. Recommend appropriate content types, formats, and timing to maximize user engagement.
A/B Testing: Plan, design, and analyze A/B tests to evaluate the effectiveness of different content variations/recommendations and make data-driven decisions on content improvements.
Stay up-to-date with the latest advancements in recommender systems, machine learning, and content analytics. Proactively propose and implement innovative approaches to improve content strategies.
Qualifications:
Education: PhD degree in Computer Science, Data Science, Statistics, Mathematics, or a related field. We can consider candidates with a master’s degree in exceptional cases.
Experience: Minimum of 3 years of experience with a focus on recommendation systems, content analysis, user behavior modeling, and predictive analytics. A track record of formulating a real world problem as a scientific problem and proposing state-of-the-art solutions.
Research experience: A track record of publishing work at top recommendation/ML conferences such as RecSys/KDD/WSDM/NeurIPS/ICML/ICLR etc.
Machine Learning Expertise: For recommendation based roles, you need to have experience in the areas of recommendation systems, learning to rank, off-policy evaluation, exploration and exploitation, reinforcement learning and A/B testing. We look for strong ML foundations both in traditional ML methods such as logistic regression, gradient boosted decision trees as well as neural networks. Experience with deep learning frameworks such as tensorflow, pytorch, and other ML tools such as scikitlearn, XGBoost etc. For content science based roles, experience in content analysis using large language models and other NLP techniques is preferred.
Programming Skills: Proficiency in programming languages such as Python, R, or similar for data analysis, manipulation, and modeling.
Big Data Tools: Familiarity with big data processing tools like Hadoop, Spark, SQL or similar is a plus.
Communication Skills: Excellent communication and presentation skills, with the ability to effectively convey complex data-driven insights to both technical and non-technical stakeholders.
Analytical Thinking: Strong problem-solving skills and a keen eye for detail, with the ability to draw meaningful conclusions from data.
Team Player: Demonstrated ability to work collaboratively in a team-oriented environment.
Passion for Content and User Experience: A genuine interest in content creation, user experience, and understanding what engages and delights users.
Preferred Additional Skills:
Experience in the entertainment, media, or digital content industry is a plus.
Familiarity with natural language processing (NLP) techniques for content analysis.
Knowledge of cloud-based data platforms such as AWS, Google Cloud, or Azure.
Join our team and help shape an outstanding content experience for our users. If you are passionate about recommender systems, content analytics, machine learning, we would love to hear from you. Apply now and be part of our exciting journey towards content excellence
Location - Remote
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bangalore, India Pocket FM Full timeAbout Pocket FMIt’s an amazing time to join Pocket FM as we continue shaping the future of entertainment globally with audio series. With a strong repository of audio series, Pocket FM has evolved as the powerhouse of entertainment.Our mission is to give wings and wisdom to stories, transforming them into a strong entertainment package. Pocket FM has grown...
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bangalore, India Pocket FM Full timeAbout Pocket FMIt’s an amazing time to join Pocket FM as we continue shaping the future of entertainment globally with audio series. With a strong repository of audio series, Pocket FM has evolved as the powerhouse of entertainment.Our mission is to give wings and wisdom to stories, transforming them into a strong entertainment package. Pocket FM has grown...
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