Director, Astra Marketing Strategy Delivery Lead
AstraZeneca · United States, Gaithersburg, Maryland
Job Summary
We are seeking a strategic, technically fluent Director to lead delivery of Astra Marketing Strategy, an AI-enabled product that uses AI-driven workflows to support diverse marketer use cases. The product spans three connected capabilities: strategy planning, strategy execution, and review of execution performance. This leader will shape the product vision, operating model, agentic architecture, and delivery roadmap that help marketers convert insights into clear strategies, translate strategies into coordinated execution plans, and use performance evidence to continuously improve decisions. The role connects commercial and brand leaders with marketing operations, data science, analytics, technology, agencies, and governance partners to deliver scalable, responsible, and measurable marketing capabilities across brands and markets.
Accountabilities:
Astra Marketing Strategy Product & Workflow Leadership
- Product Vision: Define the product strategy, target experience, operating model, and roadmap for Astra Marketing Strategy across the end-to-end marketing strategy lifecycle.
- Marketer Use-Case Design: Translate diverse marketer needs, decisions, workflows, and handoffs into intuitive, connected AI-driven capabilities across brands, markets, and channels.
- Strategy Planning: Enable structured situation assessment, customer and market insight synthesis, brand ambition, strategic priorities, objectives, audience choices, positioning, and measurement planning.
- Strategy Execution: Convert approved brand strategies into actionable initiatives, campaigns, channel and audience plans, execution briefs, milestones, ownership, dependencies, and measurable outcomes.
- Performance Review: Establish capabilities to monitor execution progress and results, compare outcomes with strategic objectives and KPIs, identify drivers and gaps, and recommend evidence-based adjustments to plans and execution.
Agentic Architecture, Data & AI Delivery
- Agentic Problem Solving: Frame complex marketing challenges as goal-driven, multi-step agentic workflows that can plan, retrieve governed information, use tools, reason over evidence, generate recommendations, and route decisions to the appropriate human owner.
- Agentic Frameworks: Design and guide modular agent architectures using orchestration, specialized agents, tool use, retrieval-augmented generation, context and memory patterns, evaluation, observability, guardrails, and human-in-the-loop controls.
- Decision Intelligence: Apply AI, machine learning, causal and predictive analytics, experimentation, optimization, and generative AI methodologies to solve marketing and data science problems across planning, execution, and performance review.
- Data & Platform Integration: Guide secure integration of governed brand, customer, market, campaign, channel, content, CRM, finance, and performance data with enterprise AI, analytics, workflow, and planning platforms.
- Human-Governed Automation: Define autonomy boundaries, evidence and confidence requirements, approval points, exception handling, and escalation paths so accountable business owners retain final decision rights.
Delivery, Operating Model & Scale
- Roadmap & Execution: Translate business priorities into architecture decisions, epics, backlogs, release plans, and measurable increments; manage dependencies across data, platforms, analytics, process, and change enablement.
- Cross-Functional Leadership: Build and lead a multidisciplinary delivery model spanning product, engineering, data science, analytics, marketing, brand strategy, marketing operations, user experience, agencies, and offshore or external partners.
- Reusable Capabilities: Establish common strategy objects, taxonomies, templates, workflow components, data products, prompts, agents, and measurement patterns that can scale while preserving brand and market context.
- Adoption & Change: Drive stakeholder engagement, training, role clarity, process guidance, and feedback loops that embed AI-driven workflows into day-to-day marketer activities across planning, execution, and performance review.
Performance, Governance & Business Partnership
- Value Measurement: Define product and business success measures such as planning cycle time, strategy quality, execution alignment, adoption, time to activation, KPI attainment, learning velocity, and decision impact.
- Responsible AI & Data Governance: Embed privacy, security, access controls, data quality, traceability, source citations, auditability, model evaluation, monitoring, and risk-based safeguards throughout the solution lifecycle.
- Performance Learning Loop: Turn execution evidence, experiments, user feedback, and market outcomes into actionable insights that improve future strategy choices, execution plans, and product capabilities.
- Stakeholder Partnership: Align brand, commercial, marketing operations, analytics, technology, finance, compliance, and market leaders on priorities, decision rights, requirements, and success criteria.
Essential Skills/Experience:
- At least a Bachelor's in marketing, business, data science, computer science, engineering, life sciences, or a related field.
- 10+ years of progressive experience in marketing strategy, product management, technology, data and AI, commercial operations, or related roles, including 3-5 years leading cross-functional teams or complex transformation programs.
- Demonstrated experience designing or delivering capabilities across strategy planning, strategy execution, and execution-performance review, with a strong understanding of how strategy decisions translate into measurable marketing activity.
- Experience designing AI-driven products and workflows that support diverse marketer use cases, from strategic planning and decision-making through operational execution and performance review.
- Demonstrated fluency in agentic problem solving and agentic AI frameworks, including orchestration, tool use, retrieval-augmented generation, context and knowledge design, memory, guardrails, human-in-the-loop controls, observability, and evaluation.
- Hands-on experience with enterprise agentic AI platforms and solutions, such as AWS AgentCore and Databricks agentic solutions.
- Proficiency in applying AI methodologies to solve data science and marketing problems, including selecting, developing, evaluating, and operationalizing machine learning, generative AI, optimization, experimentation, and analytics approaches.
- Strong understanding of marketing measurement, KPI frameworks, campaign and channel performance, experimentation, attribution, and the use of evidence to improve strategy and execution.
- Experience integrating enterprise data, analytics, AI, and workflow platforms using secure, scalable, and maintainable patterns.
- Strong stakeholder communication and change leadership skills, with a record of aligning senior business leaders, marketers, data scientists, engineers, analysts, governance teams, and external partners.
Desirable Skills/Experience:
- Experience supporting pharmaceutical, biotech, healthcare, or another highly regulated industry, including familiarity with commercial, privacy, compliance, and responsible AI requirements.
- Experience supporting brand planning, omnichannel marketing, customer engagement, marketing operations, or commercial analytics across multiple brands, therapeutic areas, markets, or customer segments.
- Experience deploying production-grade agentic or AI-enabled decision-support products with evaluation, monitoring, risk controls, and continuous improvement.
- Familiarity with enterprise ecosystems such as CRM, customer data platforms, marketing automation, campaign management, business intelligence, experimentation, and planning or performance-management tools.
The annual base pay (or hourly rate of compensation) for this position ranges from 186,014.4 to 279,021.6 USD. Our positions offer eligibi