Staff Product Manager (Recommendations)

JioStar; Bengaluru; On-site; INR 2,000,000 - 3,000,000; Full time

Job summary

JioStar is seeking a Staff Product Manager for Recommendations to enhance content discovery for its 500M+ users. You will shape product strategy, driving improvements in user engagement through personalized, ML-powered recommendations.

The ideal candidate has at least 7 years of experience in product management, with a strong background in ML and proven track record in driving measurable improvements.

Qualifications

• Minimum 7+ years of experience with at least 5 years in product management.
• At least 2 years of owning ML or recommendation products.
• Fluency in the ML product lifecycle.

Responsibilities

• Conduct research and analysis on recommendation quality and user engagement.
• Translate problem statements into clear requirements.
• Collaborate effectively with ML and Data Science teams.
• Take ownership of key KPI aligned with Product Objectives.
• Participate in team-building and hiring processes.

Skills

Communication skills and articulation

Structured frameworks and logical thinking

Understanding of ML fundamentals

Data-driven decision making

User empathy

Mentoring junior PMs

Education

Bachelor's/Master's degree or equivalent

Job description

Job Summary:

As a Staff Product Manager for Recommendations, you’ll work closely with senior product and engineering peers to shape a product strategy that drives measurable improvements in content discovery and user engagement across JioHotstar’s 500M+ user base. You will own the recommendation surfaces that define what users see the moment they open the app, spanning personalised, ML-powered recommendations as well as popularity-based and contextual content surfaces. Drawing on data, experimentation, and deep ML product instincts, you will identify gaps in personalisation quality, translate them into clear product requirements, and work with ML and engineering to ship improvements that move retention and engagement metrics at scale.

About the Team:

At JioHotstar, we’re on a mission to deliver delightful experiences to our 500+ million customers across the globe. Our technology spans 25+ countries, with more on the horizon. The Recommendations & Personalisation (P13N) team sits within the Viewer Experience org and is responsible for deciding what content each user sees across every surface of the platform.

We blend world-class engineering, ML, design, and data to deliver a seamless, personalised, and engaging streaming experience at massive scale. If you’re passionate about building intelligent, performant recommendation systems that measurably improve how hundreds of millions of users discover content, join us in shaping the future of streaming.

Key responsibilities:

• Conduct thorough research and analysis on recommendation quality, user engagement patterns, and content discovery gaps to generate actionable insights and determine ROI of personalisation investments.
• Translate abstract problem statements into clear requirements by assessing user behaviour signals, model performance, and content catalogue dynamics.
• Influence roadmaps within and outside the Recommendations team to enhance the broader personalisation ecosystem, including integrations with Search, Watch Experience, and Growth.
• Collaborate effectively with ML Engineering, Data Science, and Platform teams, contributing to roadmaps and prioritisation decisions beyond the immediate recommendations domain.
• Show agility in responding to external signals like content launches, live sports events, seasonal shifts, enabling swift roadmap adjustments and ensuring transparent communication of downstream impacts on recommendation quality.
• Take ownership of at least one key KPI that directly aligns with Product Objectives and Key Results.
• Develop deep expertise in at least one functional area, driving strategic advancements through innovation and transformation.
• Actively participate in team-building initiatives, including contributing to hiring processes.

Skills and attributes for success:

• Communicate clearly and effectively, with precise articulation and logical progression of ideas, equally comfortable writing ML-facing product requirements and presenting recommendation strategy to senior leadership.
• Apply structured frameworks and logical thinking to address complex personalisation problems, asking the right questions to determine whether an issue is rooted in data quality, model behaviour, or product definition.
• Demonstrate the ability to bridge business problems and ML solutions, translating user and product goals into well‑scoped ML problem formulations, and conversely, grounding algorithmic improvements in measurable business or user outcomes.
• Demonstrate sufficient understanding of ML fundamentals, including how recommendation models are trained, evaluated, and deployed, to have substantive technical conversations with Data Scientists and ML Engineers, challenge assumptions, and make informed prioritisation decisions.
• Leverage a data‑driven mindset to derive insights from metrics and make well‑informed product decisions.
• Show strong user empathy, using structured problem‑solving to address pain points through hypothesis‑building and solution development.
• Benchmark competitors and validate findings through experimentation, maintaining high standards for product roadmap inclusion.
• Confidently tackle ambiguous problems with minimal supervision, taking decisive action across the organization.
• Foster a collaborative, respectful work environment, open to feedback, while mentoring junior PMs to success and effectively influencing without formal authority.

Preferred education and experience:

• Bachelor’s/Master’s degree or equivalent preferred. Minimum 7+ years of experience with at least 5 years of experience in product management.
• At least 2 years owning ML or recommendation products at consumer scale, with a demonstrable track record of shipping measurable metric improvements, not just features.
• Fluency in the ML product lifecycle: feature engineering, A/B experiment design, metric interpretation, and launch readiness.