Director, Astra Marketing Strategy Delivery Lead

AstraZeneca•Gaithersburg, MD

12d•$186,014 - $279,022•Hybrid

About The Position

AstraZeneca is seeking a strategic, technically fluent Director to lead delivery of Astra Marketing Strategy, an AI-enabled product supporting diverse marketer use cases. The product connects strategy planning, strategy execution, and execution-performance review. The Director will shape the product vision, operating model, agentic architecture, and delivery roadmap so marketers can turn insights into strategies, coordinate execution, and use performance evidence to improve decisions. The role connects commercial and brand leaders with marketing operations, data science, analytics, technology, agencies, and governance partners to deliver scalable, responsible, measurable capabilities across brands and markets. The role leads product and workflow design across the marketing strategy lifecycle; develops agentic AI, data, and platform capabilities; and establishes delivery, governance, adoption, and measurement practices. It involves aligning multidisciplinary teams and business stakeholders, integrating governed enterprise data, and ensuring human oversight, responsible AI safeguards, and continuous learning from performance evidence.

Requirements

Bachelor’s degree or higher in marketing, business, data science, computer science, engineering, life sciences, or a related field.
At least 10 years of progressive experience in marketing strategy, product management, technology, data and AI, commercial operations, or related roles.
Three to five years leading cross-functional teams or complex transformation programs.
Demonstrated experience designing or delivering capabilities across strategy planning, strategy execution, and execution-performance review, and understanding how strategy decisions translate into measurable marketing activity.
Experience designing AI-driven products and workflows for marketer use cases spanning strategic planning, decision-making, operational execution, and performance review.
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 applying AI methodologies to 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 using 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, including a record of aligning senior business leaders, marketers, data scientists, engineers, analysts, governance teams, and external partners.

Nice To Haves

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.

Responsibilities

Define Astra Marketing Strategy’s product strategy, target experience, operating model, and roadmap across the marketing strategy lifecycle.
Translate marketer needs, decisions, workflows, and handoffs into connected AI-driven capabilities across brands, markets, and channels.
Enable structured strategy planning, including situation assessment, insight synthesis, brand ambition, priorities, objectives, audience choices, positioning, and measurement planning.
Convert approved brand strategies into initiatives, campaigns, channel and audience plans, execution briefs, milestones, ownership, dependencies, and measurable outcomes.
Establish capabilities to monitor execution progress and results, assess outcomes against strategic objectives and KPIs, identify drivers and gaps, and recommend evidence-based adjustments.
Frame marketing challenges as goal-driven, multi-step agentic workflows that retrieve governed information, use tools, reason over evidence, generate recommendations, and route decisions to human owners.
Design and guide modular agent architectures incorporating orchestration, specialized agents, tool use, retrieval-augmented generation, context and memory, evaluation, observability, guardrails, and human-in-the-loop controls.
Apply AI, machine learning, causal and predictive analytics, experimentation, optimization, and generative AI to marketing and data science problems.
Guide secure integration of governed brand, customer, market, campaign, channel, content, CRM, finance, and performance data with enterprise AI, analytics, workflow, and planning platforms.
Define autonomy boundaries, evidence and confidence requirements, approvals, exception handling, and escalation paths that preserve accountable business owners’ decision rights.
Translate business priorities into architecture decisions, epics, backlogs, release plans, and measurable increments; manage dependencies across data, platforms, analytics, process, and change enablement.
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.
Establish reusable strategy objects, taxonomies, templates, workflow components, data products, prompts, agents, and measurement patterns that scale while retaining brand and market context.
Drive stakeholder engagement, training, role clarity, process guidance, and feedback loops to embed AI-driven workflows in marketers’ daily activities.
Define product and business success measures, including planning cycle time, strategy quality, execution alignment, adoption, time to activation, KPI attainment, learning velocity, and decision impact.
Embed privacy, security, access controls, data quality, traceability, source citations, auditability, model evaluation, monitoring, and risk-based safeguards throughout the solution lifecycle.
Use execution evidence, experiments, user feedback, and market outcomes to improve future strategy choices, execution plans, and product capabilities.
Align brand, commercial, marketing operations, analytics, technology, finance, compliance, and market leaders on priorities, decision rights, requirements, and success criteria.