New opportunity

Principal PMT - International, Prime Video Personalization & Discovery

Amazon · Bengaluru, India

About this role

Amazon is seeking an experienced Principal Product Manager Tech to own the vision, strategy, and roadmap for personalized content discovery for international markets within Prime Video. In this role, you will operate at the intersection of machine learning, customer experience, and business strategy to ship ML-powered features at scale.

Responsibilities

Own the roadmap and strategy for personalization products for INTL territories, including recommendation systems and ML/science-based models.
Collaborate with business leaders, other PMs and Applied Scientists to translate business problems into model requirements and convert them into clear algorithmic objectives, metrics, and guardrails.
Define, track, and analyze key metrics to measure recommendation quality, customer outcomes, and business impact; analyze user behavior data to identify opportunities to improve personalization.
Lead A/B testing strategy and experimentation to validate algorithmic improvements and inform roadmap decisions.
Partner with stakeholder teams to understand business needs and translate them into technical specifications and prioritized work.
Communicate complex technical and algorithmic concepts clearly to senior leadership and cross-functional partners.
Balance trade-offs between model complexity, latency, scalability, and business value to ensure robust, production-ready solutions.

Required qualifications

10+ years of technical Product Management experience, or 7+ years of PM experience and 3+ years of Engg experience.
6+ years of end to end product delivery experience
Bachelor's degree
Experience with feature delivery and tradeoffs of a product
Experience owning/driving roadmap strategy and definition
Experience leading engineering discussions around technology decisions and strategy related to a product
Experience managing consumer-facing products where ML directly impacts user experience and engagement.
Background in an applied science, engineering, or quantitative field, with a solid understanding of the model lifecycle, data readiness, and model evaluation frameworks.

Preferred qualifications

Masters degree preferred
Experience in content discovery, e-commerce, search, or recommendation systems
Knowledge of modern ML techniques (deep learning, NLP, LLM, Agentic AIs) and their practical trade-offs.
Strong leadership skills with ability to influence cross-functional teams
Experience working with senior-level stakeholders
Experience in project management methodologies, business analysis, or process improvement

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

Machine LearningRecommendation SystemsProduct ManagementA/B TestingDeep LearningNLPLLMAgentic AIsData Science

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Own the International Personalization Vision and Roadmap
Formulate and drive the product strategy and roadmap for personalized content discovery specifically tailored to international territories within Prime Video, ensuring alignment with broad business goals and customer value.
Translate Business Problems into Algorithmic Objectives
Collaborate closely with Applied Scientists and engineering teams to translate ambiguous customer and business requirements into clear algorithmic objectives, metrics, feature requirements, and model guardrails.
Drive A/B Testing and Experimentation Strategy
Lead rigorous experimentation and A/B testing frameworks to validate algorithmic improvements, measure recommendation quality, and inform ongoing roadmap iterations and deployment decisions.
Balance Technical Trade-offs and Model Scale
Navigate trade-offs between model complexity, latency, scalability, and business value to deliver robust, high-performance production-ready personalization solutions for large-scale streaming audiences.

Likely challenges to think through

Bridging Business Ambiguity and Technical Algorithmic Requirements
The challenge lies in taking ambiguous customer goals in international entertainment markets and converting them into precise machine learning and science definitions. Prepare by practicing structured problem decomposition and framing clear metrics that map business value directly to model performance.
Balancing Model Complexity with Streaming Latency and Scale
Deploying advanced recommendation and deep learning models for a high-traffic media streaming service requires strict latency constraints. Prepare by reviewing system design trade-offs, inference optimization strategies, and methods to balance sophisticated personalization algorithms with real-time performance.

Practice your answers

Technical practice

Translating Business Strategy into Algorithmic Metrics

What to coverDiscuss how you translate ambiguous business goals into concrete machine learning objectives and success metrics. Guidance: Candidates should explain how they collaborate with applied scientists to establish recommendation quality metrics, engagement guardrails, and business KPIs that balance user satisfaction with platform goals.

Balancing Model Complexity and Streaming Latency

What to coverHow do you evaluate and balance trade-offs between model complexity, inference latency, and business value for real-time recommendations? Guidance: Address how sophisticated deep learning models or LLMs impact response times on consumer devices, and discuss strategies to maintain high scalability and low latency without sacrificing recommendation accuracy.

Designing A/B Testing Frameworks for Personalization

What to coverDescribe your approach to designing and executing A/B testing strategies for algorithmic updates in a streaming product. Guidance: Focus on traffic allocation, handling network effects, defining primary versus guardrail metrics, and using experiment results to guide iterative product roadmaps.

Managing the Machine Learning Lifecycle

What to coverWhat is your approach to guiding sound science decisions throughout the ML lifecycle, from problem framing and data readiness to deployment? Guidance: Highlight your understanding of data readiness, offline evaluation frameworks, online experimentation, and ongoing model monitoring and optimization.

Applying Modern ML Techniques in Content Discovery

What to coverHow can modern machine learning techniques such as deep learning, NLP, LLMs, or agentic AIs be practically applied to content discovery and recommendation systems? Guidance: Discuss the practical trade-offs, computational costs, and user experience benefits of incorporating advanced generative and neural techniques into existing recommendation pipelines.

Defining Product Roadmaps for International Markets

What to coverHow do you approach setting a long-term product vision and roadmap for diverse international territories with unique customer behaviors? Guidance: Explain how you analyze user behavior data, identify regional content discovery opportunities, and prioritize engineering efforts to scale products globally.

Evaluating Recommendation System Quality

What to coverWhat offline and online evaluation frameworks do you use to assess recommendation quality and user engagement? Guidance: Discuss metrics such as relevance, diversity, novelty, click-through rate, and retention, along with how offline metrics correlate with actual customer business outcomes.

Cross-Functional Collaboration with Applied Scientists

What to coverHow do you effectively partner with applied scientists and engineering teams to turn technical possibilities into production-ready features? Guidance: Emphasize your ability to lead technology discussions, bridge the gap between business requirements and functional engineering specs, and maintain shared accountability for delivery.

Managing Consumer-Facing Product Trade-offs

What to coverCan you share an example of managing feature delivery trade-offs where machine learning directly impacts the user experience? Guidance: Discuss how you weigh user friction, interface design constraints, and algorithmic performance to ensure complex technical capabilities translate into intuitive customer experiences.

Data Readiness and Feature Engineering Strategy

What to coverHow do you assess data readiness and ensure robust data pipelines are in place before launching new ML-powered features? Guidance: Address data quality, feature store considerations, feedback loops, and how data readiness directly influences model convergence and deployment success.

Behavioural practice

Influencing Senior Stakeholders Without Authority

What to coverDescribe a situation where you had to influence senior leadership and cross-functional partners to adopt a complex technical strategy or product roadmap. Guidance: Use the STAR method to describe the situation, your communication approach, how you addressed technical skepticism, and the resulting business impact.

Resolving Prioritization Conflicts Across Teams

What to coverTell me about a time when competing priorities between business growth, engineering scalability, and data science goals caused friction. Guidance: Detail how you analyzed trade-offs, communicated clearly with stakeholders, and drove alignment to achieve high-impact product delivery.

Delivering High-Scale Products under Tight Deadlines

What to coverGive an example of a fast-paced, high-impact technical product you successfully delivered end-to-end. Guidance: Explain your role in owning the roadmap, navigating ambiguity, managing engineering trade-offs, and measuring customer outcomes post-launch using structured metrics.

Handling Experimentation Failure or Inconclusive Results

What to coverDescribe a scenario where an A/B test or major algorithmic release failed to show the expected business improvement. Guidance: Explain how you analyzed user behavior data, diagnosed the root cause with science teams, and adapted your product strategy moving forward.

Aligning Cross-Functional Teams Around a Complex Vision

What to coverShare an experience where you aligned product managers, engineers, and applied scientists around a unified vision for a consumer-facing feature. Guidance: Highlight how you translated ambiguous customer problems into clear technical requirements and fostered a high level of execution and focus.

Sharpen your knowledge

Topics to prepare

Recommendation Systems and Personalization Science
Review fundamental concepts of content discovery, collaborative filtering, deep learning models, and ranking architectures. Exercise: Write a product requirements document (PRD) outlining how to improve video recommendations for a diverse international audience while balancing relevance and novelty. Relevance: Directly addresses the core requirement to own the personalization roadmap and science-based models for INTL markets.
Machine Learning Lifecycle and Evaluation Frameworks
Study the end-to-end ML lifecycle from problem framing, data readiness, and feature engineering to deployment and model evaluation. Exercise: Draft an evaluation framework defining offline metrics and online guardrails for a new streaming recommendation model. Relevance: Fulfills the requirement for a solid understanding of model evaluation frameworks and guiding sound science decisions.
A/B Testing and Experimentation Methodology
Examine statistical principles of online experimentation, traffic splitting, power calculations, and metric trade-offs. Exercise: Design an A/B testing plan for a personalization feature update, detailing primary customer metrics and guardrail metrics to monitor latency impact. Relevance: Aligns with the explicit requirement to lead A/B testing strategy and experimentation to validate algorithmic improvements.

Skills to revise

Translating Business Problems into Algorithmic Objectives
Practice converting ambiguous business goals into rigorous technical specifications and model targets. Exercise: Take a high-level business goal like increasing engagement in an international market and map it into explicit algorithmic objective functions, metrics, and trade-offs. Relevance: Directly supports collaborating with Applied Scientists to translate business needs into model requirements.

Your preparation checklist

Draft a Product Strategy and Roadmap for International Personalization
Create a comprehensive sample product roadmap document outlining vision, strategy, and phased milestones for personalized content discovery in international media streaming markets, reflecting customer value and business outcomes.
Build an ML-to-Business Metric Translation Matrix
Prepare a structured framework that maps ambiguous business goals and customer problems into clear algorithmic objectives, success metrics, experimentation guardrails, and quality targets for recommendation systems.
Develop an A/B Testing and Experimentation Design Plan
Write out a rigorous experimentation plan detailing traffic allocation, handling network effects, defining primary versus guardrail metrics, and analyzing user behavior data to validate algorithmic improvements.
Document a Technical Trade-off Analysis for Model Latency and Complexity
Prepare a written analysis examining the trade-offs between model complexity, inference latency, scalability, and business value when deploying machine learning models in consumer-facing streaming products.
Prepare Leadership and Cross-Functional Alignment Stories
Outline specific past experiences using the STAR method that demonstrate your ability to lead cross-functional efforts, guide engineering discussions around technology decisions, and influence senior-level stakeholders.

Availability last checked: 4 Nov 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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