Senior Staff Product Manager - Technical (ai/ml), Data & Audience Platform Team, Hyderabad

Warner Bros Discovery Warner Bros Discovery · Media · Hyderabad, Telangāna, India · Technology

Senior Staff Product Manager - Technical (AI/ML) for the Data & Audience Platform Team. This role is responsible for defining and driving the AI roadmap across predictive ML, GenAI, agentic workflows, AI-enabled analytics, data activation, and AI-native platform capabilities. The focus is on elevating the platform into an AI enablement function, identifying high-value AI opportunities, selecting appropriate technologies, and ensuring AI investments are actionable, safe, and outcome-driven. Key responsibilities include shaping AI product strategy, evaluating AI/ML opportunities, writing AI-focused PRDs, partnering with engineering and data science on tradeoffs, defining KPIs, and translating complex AI concepts into executive narratives.

What you'd actually do

  1. Shape AI product strategy and roadmap priorities across DAP platform areas.
  2. Evaluate AI/ML opportunities through value, feasibility, cost, data readiness, governance, and reuse lenses.
  3. Write AI-focused PRDs, product briefs, model/agent behavior requirements, evaluation criteria, and launch readiness requirements.
  4. Partner with engineering, data science, and architecture to compare model, platform, vendor, and implementation tradeoffs.
  5. Define KPIs and decision frameworks that connect AI outputs to business outcomes.

Skills

Required

  • 12+ years of product management, technical product management, AI/ML product strategy, data platform, analytics, or related experience.
  • Deep experience defining and delivering AI/ML, GenAI, data platform, customer intelligence, analytics, activation, or enterprise platform products at scale.
  • Strong working knowledge of AI/ML product concepts including model lifecycle, experimentation, model evaluation, MLOps, data quality, governance, semantic layers, feature/data products, and post-launch performance measurement.
  • Practical fluency in modern AI patterns such as LLMs, RAG, agentic systems, AI orchestration, prompt and agent lifecycle management, human-in-the-loop workflows, and AI observability.
  • Demonstrated ability to set product vision and direction across multiple teams and highly ambiguous technical domains.
  • Strong ability to evaluate technology tradeoffs across capability, quality, scalability, privacy, security, cost, maintainability, and business value.
  • Excellent executive communication skills with the ability to translate technical AI concepts into clear decisions, narratives, and value cases.
  • Proven ability to influence without authority across engineering, architecture, data science, TPM, analytics, operations, business, governance, and senior leadership teams.
  • Track record of delivering measurable customer, business, operational, or platform impact from technical product investments.

Nice to have

  • Experience with streaming media, digital video, Customer 360, personalization, recommendations, content intelligence, advertising analytics, audience activation, commerce analytics, marketing analytics, or global platform products.
  • Experience with AI governance, responsible AI, model risk review, privacy/security requirements, or compliance needs for AI-powered products.
  • Experience creating scalable intake, prioritization, value management, and post-launch measurement mechanisms for AI portfolios.
  • Experience with AI cost management, cloud/data platform FinOps, model inference cost tradeoffs, vendor evaluation, or build/buy/reuse

What the JD emphasized

  • AI/ML product strategy
  • AI/ML
  • GenAI
  • agentic workflows
  • AI-enabled analytics
  • data activation
  • AI-native platform capabilities
  • AI roadmap
  • AI opportunities
  • AI investments
  • AI product strategy
  • AI/ML opportunities
  • AI-focused PRDs
  • model/agent behavior requirements
  • AI roadmap
  • AI concepts
  • AI work
  • AI cost management

Other signals

  • Define and drive AI roadmap
  • AI-centric role
  • Product-led AI enablement
  • Identify highest-value AI opportunities
  • Convert experimentation into durable business outcomes
  • Disciplined AI product strategy
  • Select right problems
  • Validate newest technology is best fit
  • Define measurable business value
  • Ensure platform investments are reusable, governed, cost-aware, and scalable
  • Work across DAP, engineering, analytics, data science, architecture, product, operations, privacy/legal, AI governance, and senior leadership
  • Make AI investments actionable, safe, and outcome driven