Paramount

• Design, implement, and manage scalable and reliable Kubernetes-based infrastructure for personalization services. • Build and own CI/CD pipelines that ship services (and ML models) to production safely — canary, rollback, progressive delivery. • Stand up observability and monitoring with Prometheus, New Relic, OpenTelemetry, and Grafana; define SLIs / SLOs and drive error-budget discipline. • Ensure high availability, security, and performance of production APIs and streaming data pipelines. • Partner with application, data, and ML engineers to integrate their workloads smoothly into the platform. • Implement autoscaling strategies (HPA, KEDA, traffic-driven) for variable, bursty traffic patterns. • Manage Pub/Sub and event-driven architectures for real-time messaging, engagement analytics, and inter-service communication. • Optimize hot-path services using Redis, Memcached, and other caching strategies. • Debug and tackle production issues around latency, scaling, and reliability.

• Lead and manage land and title projects from scoping through completion across multiple basins and jurisdictions. • Serve as the primary point of contact for clients, providing consistent, clear, and proactive communication throughout the life of each project. • Build and manage project timelines, staffing plans, and budgets, adjusting quickly as project scope or client needs evolve. • Recruit, assign, and oversee teams of landmen and abstractors, ensuring work is completed accurately and on schedule. • Review and quality control title work product, including runsheets, curative documents, and ownership reports, to ensure it meets Paramount and client standards. • Interpret and resolve complex leasehold title issues, including chain of title gaps, HBP determinations, and multi party ownership questions. • Coordinate with landmen, attorneys, and clients to move curative items to resolution quickly. • Track project progress and provide regular status updates and deliverables to clients in a professional and organized format. • Identify risk and title issues early and communicate practical solutions before they become project delays. • Maintain a strong working knowledge of basin specific regulations, recording practices, and county level nuances across project areas.

• Direct the overarching strategy for "Paramount Takeovers" across the U.S. retail landscape • Ensure global beats for major franchises are localized and executed with consistency across all account-specific teams • Partner with the Retail Sales organization to develop future-focused Joint Business Planning (JBP) • Lead executive-level presentations to retail buyers to secure exclusive cross-category statements and premium placements • Lead comprehensive marketing plans inclusive of in-store, e-commerce, and Retail Media Networks (RMNs) • Integrate Paramount+ into the shopper journey through high-value bundles (e.g., Walmart+ or Amazon Prime) • Champion "First-to-Market" retail experiences, including livestream commerce, AR-driven in-store activations, and experiential pop-ups to differentiate brands at the point-of-purchase • Act as the internal "Voice of the Retailer," ensuring retailer needs and insights are integrated into brand strategies • Navigate a complex matrix (Studio, Licensing, and Ad Sales) to ensure retail mandates are fully adopted • Develop measurable frameworks and dashboards to optimize programs using A/B testing, ROI metrics, and consumer insights • Own the total U.S. Retail Marketing budget, including tracking, reconciliation, and ROI-based allocation • Lead agency briefing and creative oversight to ensure all elements meet brand and legal guidelines • Build and mentor a high-performing team by setting clear KPIs and fostering a culture of creativity, accountability, and inclusivity

• Conduct deep-dive analyses to generate insights that inform content strategy, product experience, and growth initiatives • Design and operationalize frameworks and models that link user activity (content consumption, platform usage) to downstream subscriber lifecycle outcomes • Identify churn improvement opportunities and risk areas through proactive analysis • Translate complex analytical findings into clear, compelling narratives for executive leadership and cross-functional stakeholders • Deliver fast, ad-hoc analyses to address evolving business needs and priorities • Partner with our Research team to integrate qualitative knowledge with quantitative models and metrics • Partner closely with Data Engineering to design scalable pipelines, data models, and dashboards • Integrate AI-driven tools and automation into daily workflows to enhance efficiency, scale reporting, and unlock deeper performance insights

• Serve as the established technical owner of one or more core CMS systems (API layer, integration services, search pipeline, or admin UI) • Drive architectural decisions across the CMS platform, including API design, data modeling, event-driven integrations, and search infrastructure • Lead the design and implementation of scalable, robust services across a polyglot stack: Java/Spring Boot APIs, Next.js/React frontends, Python services, Kafka/PubSub messaging, and Solr search pipelines • Define and promote engineering best practices • Explain what team members do well and where they need supervision • Identify, prioritize, and strategically reduce technical debt across the domain • Write and review RFCs, architecture decision records, and technical design documents for significant changes • Set the team's bar for AI-assisted development — including review standards for agent-generated PRs, prompt and context patterns. • Stay involved in coding. Write important production code. • Lead large projects and initiatives from conception through delivery — break down ambiguous, large-scale problems into actionable work • Assess technical risk, make pragmatic trade-off decisions, and communicate them clearly • Own end-to-end delivery: from design through production observability and reliability • Set a high bar for quality through automated testing, peer review, CI/CD pipelines, and observability • Mentor and coach engineers through code reviews, design sessions, pairing, and architectural guidance.

• lead the Ads Pod in architecting intelligent ad systems • develop ML strategies for ad-tech stack • create optimization models balancing watch time and revenue • build retrieval and ranking systems for personalized channel discovery • develop models for dynamic scheduling and channel creation • lead EPG personalization vision

• Manage the development and improvement of AI-driven platforms, systems, and capabilities. • Oversee a portfolio of AI/ML investments. • Lead a team of senior program management leaders. • Serve as a trusted advisor to executive stakeholders. • Partner to establish and track portfolio‑level success metrics.

• Conduct deep-dive analyses to generate insights that inform content strategy, product experience, and growth initiatives • Design and operationalize frameworks and models that link user activity (content consumption, platform usage) to downstream subscriber lifecycle outcomes • Identify churn improvement opportunities and risk areas through proactive analysis • Translate complex analytical findings into clear, compelling narratives for executive leadership and cross-functional stakeholders • Deliver fast, ad-hoc analyses to address evolving business needs and priorities • Partner with our Research team to integrate qualitative knowledge with quantitative models and metrics • Partner closely with Data Engineering to design scalable pipelines, data models, and dashboards • Integrate AI-driven tools and automation into daily workflows to enhance efficiency, scale reporting, and unlock deeper performance insights

• Implement and maintain features for tvOS and iOS applications with a focus on performance and user experience. • Write clear, maintainable Swift code that aligns with team conventions and platform best practices. • Collaborate with product, design, and backend engineering teams to refine requirements and deliver robust solutions. • Debug issues across the stack—from UI edge cases to data integration—and contribute fixes that improve app stability. • Participate in architectural discussions and follow established patterns for consistency and scalability. • Stay current with iOS platform updates and apply relevant improvements to the codebase as needed. • Review peer code for quality, readability, and adherence to standards, contributing to team knowledge-sharing. • Ensure accessibility and localization considerations are applied throughout development. • Work within an Agile environment to prioritize tasks, communicate progress, and deliver updates incrementally.

• Own ML production reliability strategy • Define and lead the operational strategy for production ML systems, including monitoring, traceability, deployment safety, incident response, and post-deployment validation. • Set the standards ML teams use to assess model health, performance, and trustworthiness in production. • Own model traceability and governance • Ensure every production model has clear lineage (data, features, code, artifacts, validation, deployment history) and drive adoption of model registry and metadata tooling across ML teams. • Build end-to-end ML observability • Design and implement monitoring across the full ML signal path: data arrival, feature freshness, distribution stability, candidate generation, ranking behavior, model metrics, serving latency, and SLA performance. • Define production health metrics • Partner with ML, data, product, and business stakeholders to define post-deployment metrics covering model quality, system reliability, business guardrails, and degradation indicators. • Detect drift and degradation proactively • Detect data drift, feature drift, model behavior changes, and silent failures before they impact customers via thresholding, alerting, anomaly detection, and release-over-release monitoring. • Lead diagnostic tooling and root-cause analysis • Build dashboards, logs, and diagnostic workflows that progress quickly from “recommendations look off” to root cause, with context captured across candidates, features, scores, ranking decisions, and downstream outcomes. • Own ML deployment safety • Define and operate automated gates that prevent bad models or bad data from being promoted to production. • Partner with MLEs to establish validation checks, rollback criteria, canary strategies, shadow testing, and release health reviews. • Lead ML incident response • Own incident response practices for ML systems, including rollback playbooks, hotfix strategies, severity definitions, tradeoff frameworks, communications, and post-mortems. • Drive closure of systemic gaps after incidents rather than only resolving the immediate issue. • Partner across ML Platform, Data, and ML • Partner with DevOps/Platform on infrastructure and observability needs; with Data Engineering on data quality, drift, and freshness; and with ML Engineering to embed operational requirements into development and deployment workflows. • Set standards and mentor others • Act as the technical lead for ML operations: establish reusable patterns, playbooks, and standards, and mentor engineers on reliability, observability, and operational rigor.