Overview

Welcome to Warner Bros. Discovery… the stuff dreams are made of.

Responsibilities

• Directly manage 7 ICs (2× MLE 2, 3× Senior MLE, 1× Staff MLE, 1× Staff DS); own their performance, growth, and career development.
• Establish a high-performance engineering culture: technical excellence, ownership, psychological safety, rigorous experimentation, and continuous learning.
• Define clear role expectations, growth paths, and promotion criteria aligned with WBD’s global ML engineering job family; conduct regular 1:1s, performance reviews, and career conversations, addressing performance gaps proactively.
• Lead hiring for the Hyderabad team: define the hiring bar, design interview processes, attract top ML talent, and build a strong, diverse local pipeline.
• Foster an inclusive, collaborative environment that bridges India and US time zones effectively.
• Maintain sufficient technical depth to review ML architecture proposals, challenge design decisions, and unblock technical escalations across all workstreams.
• Partner with the Staff MLE to set and enforce technical standards: MLOps practices, code quality, model-evaluation rigor, and documentation.
• Own the technical roadmap for the team’s ML systems — identity intelligence, audience intelligence, content affinity, forecasting — in alignment with global ML strategy.
• Drive adoption of the team’s core stack: Databricks (primary), AWS SageMaker, Snowflake, MLflow, and agentic AI tooling (Cursor, Copilot, Amazon Q, Databricks Genie, Snowflake Cortex, MCP).
• Ensure production ML systems are reliable, monitored, cost-efficient, and continuously improved; own the team’s on-call and incident-response posture.
• Own end-to-end delivery accountability for Hyderabad’s ML projects: scope, milestones, dependencies, risks, and stakeholder communication.
• Run effective agile processes adapted to ML realities (research uncertainty, data dependencies, experiment iteration), with D90 planning rhythms and quarterly OKR alignment.
• Proactively identify and resolve cross-team dependencies with Data Engineering, Product, Ad Sales, and Legal/Privacy.
• Maintain a clear view of capacity, prioritization trade-offs, and commitments; communicate status and risks transparently to the Director.
• Identity Intelligence: oversee delivery of the probabilistic ID spine — resolving unauthenticated signals (device IDs, 1P cookies) to households/persons with calibrated confidence across all WBD brands.
• Audience Intelligence: oversee ML Promo Optimizer, STAT v2 (single-title affinity retrieval), lookalike modeling (LAL 2.0+ in Snowflake DCR), and segmentation that power advertising and marketing activation.
• Content Affinity & Personalization: oversee genre-preference and content-affinity models and engagement-based personalization signals feeding HBO Max and CNN surfaces.
• Forecasting: oversee ML-based audience, engagement, and yield/revenue forecasting supporting Finance, Content, and Marketing planning.
• MLOps Platform: ensure the team’s feature store, training/serving pipelines, model registry, and monitoring are production-grade, well-documented, and consistently adopted.
• Serve as the primary point of contact for the Hyderabad ML team across Product, Marketing, Ad Sales, Data Engineering, Legal/Privacy, and Finance.
• Build strong relationships with US-based ML leadership, Data Science, and Platform Engineering counterparts so Hyderabad is a fully integrated — not siloed — part of the global ML organization.
• Partner with the Director on long-range team strategy, capability planning, and organizational design; represent WBD ML externally where appropriate (talent branding, university partnerships).
• Ensure ML systems meet privacy, governance, and compliance requirements.
• Champion agentic AI development: establish norms for using Cursor, GitHub Copilot, Amazon Q, and MCP-based tooling to accelerate ML velocity, with measurable improvements in quality and lifecycle efficiency.
• Drive strategic adoption of Databricks Genie (governed, self-service NL interfaces over Unity Catalog) and Snowflake Cortex (Copilot/Analyst, Cortex Search, and selective Fine-Tuning) so ML outputs are accessible to non-technical stakeholders.
• Create space for innovation: structured prototyping time, internal tech talks, and contribution to WBD’s broader ML community of practice; bring relevant external insights into the roadmap.

Qualifications

• 12+ years of total experience in ML, data science, or ML engineering, including 3–5+ years in engineering management.
• Demonstrated success leading and growing teams of 6–12 ML engineers and/or data scientists, including a multi-level IC ladder (junior through Staff).
• Strong technical foundation in ML: you can read and critique model-architecture proposals, review ML code, and engage credibly in Staff-level discussions — you have personally built and shipped production ML.
• Hands-on (current or recent) experience with the ML stack: Python, Databricks/Spark, MLflow, cloud ML (AWS SageMaker preferred), and SQL/Snowflake.
• Proven track record delivering complex, multi-month ML projects in production at scale.
• Excellent communication: translating technical complexity for executive and non-technical audiences, and business context for engineers.
• Bachelor’s or Master’s degree in Computer Science, Statistics, Engineering, or a related quantitative field.
• ML leadership in streaming, media, ad-tech, or consumer internet — particularly audience modeling, identity resolution, personalization, or recommendations.
• Familiarity with WBD’s ecosystem: Databricks, Snowflake, AWS, FreeWheel/GAM, or equivalent ad-tech activation stacks.
• Familiarity with Databricks Genie, Snowflake Cortex, MCP, and modern AI-assisted development workflows.

Benefits

• A Great Place to work
• Equal opportunity employer
• Fast track growth opportunities