AstraZeneca

Director, Oncology Commercial Data Science & AI Products

AstraZeneca • $186K — $279K *
Gaithersburg, MD 20878In-Person
Pharmaceuticals & Biotech
5 - 7 years of experience
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Job Overview by Ladders

Qualifications

  • Bachelor's degree in a quantitative field; advanced degrees like MBA or PhD preferred.
  • 7-10+ years of experience in pharma with minimum 5 years in commercial functions; proven AI/product ownership in oncology.
  • Strong analytical skills to bridge brand strategy and data science.
  • Hands-on experience with LLMs and AI tooling; proficiency with Python and major cloud platforms.
  • Deep familiarity with oncology commercial data sources and healthcare analytics.
  • Exceptional communication and relationship-management skills in matrixed organizations.
  • Proactive leader with capability to align cross-functional teams toward business goals.

Responsibilities

  • Own and strategize the AI transformation roadmap for Sales, Marketing, and Medical Affairs.
  • Develop and present quantifiable value cases for AI capabilities to leadership.
  • Maintain a prioritized product backlog, balancing innovation and operational needs.
  • Track market innovation and competitor AI initiatives to inform roadmap direction.
  • Lead the development of GenAI applications tailored for commercial and medical teams.
  • Collaborate with cross-functional teams to create actionable AI use cases and user stories.
  • Set the direction for AI capability building and change management among sales and marketing.

Benefits

  • Opportunities for professional growth and advanced learning.
  • Access to cutting-edge AI and tech resources in a leading pharma environment.
  • Collaborative work culture fostering innovation and transformation.
  • Comprehensive training plans and workshops to enhance AI literacy across teams.
Full Job Description
Role overview
This is a product ownership and program strategy role - not an analytics consulting role. You lead the full AI product lifecycle: problem definition, customer research, product requirements, AI development partnership, UX design alignment, deployment, change management, and post-launch impact measurement. You are the product owner and strategist for the AI capabilities powering Sales, Marketing, and Medical Affairs across your assigned OBU tumor areas, and the liaison US Oncology and the broader AstraZeneca Enterprise AI organization for customer engagement capability building.
You will be equally comfortable writing a product requirements document, reviewing a GenAI architecture with an engineering lead, running a discovery session with brand managers, delivering the AI transformation narrative to field sales leadership and presenting a capability roadmap to the Senior Leadership Teams. You will bring a platform-centric, reuse-first mindset: every solution you ship should be architected for scalability and accelerated deployment across indications - so a trigger framework proven in one tumor area becomes the template for the next launch.

Key responsibilities

1. Program Strategy & AI Roadmap Ownership
  • Own the strategy and transformation roadmap for Sales, Marketing, and Medical Affairs AI capabilities across your assigned OBU tumor-area portfolio, being responsible for planning and budget management processes in alignment with OBU annual cycle.
  • Develop quantifiable cases and value narratives for future Sales, Marketing, and field AI capabilities in collaboration with OBU Franchises, I&A, and Enterprise AI delivery teams; present to OBU Leadership Team (OLT) to drive top-down alignment and shared decisions on roadmap prioritization.
  • Maintain a prioritized product backlog with well-defined user stories, acceptance criteria, and delivery timelines; balance the portfolio across housekeeping (maintenance/refresh), innovation (new AI capability pilots), and new-indication onboarding in an agile operating model.
  • Track industry, market, and innovation shifts - GenAI, agentic AI, oncology data science, competitor AI strategies - to anticipate opportunities and risks and keep the roadmap forward-looking.
  • Champion a platform-centric, reuse-first architecture philosophy: design AI capabilities that pilot in one indication and scale across OBUs without re-platforming.

2. GenAI & Agentic AI Product Development
  • Lead the definition, design, and delivery of GenAI-powered applications for OBU commercial and medical teams: AI-assisted field briefing tools (InsightIQ), clinical evidence summarization for MSLs, agentic omnichannel workflows (Engagement IQ), and natural-language interfaces to AZBrain analytics.
  • Define product requirements and functional specifications for LLM-powered, RAG-based, and agentic AI applications; partner with Enterprise AI engineering leads to translate requirements into governed, scalable, production-grade solutions.
  • Maintain solid understanding of the evolving GenAI / agentic AI landscape - prompt engineering, RAG architectures, multi-agent orchestration, evals - and critically evaluate architecture choices against OBU field workflow requirements.
  • Apply rigorous pilot-and-scale methodology: define pilot scope and success criteria upfront, measure output quality and user adoption, and drive evidence-based scaling decisions to other indications and OBU functions.

3. DS&AI Business Partnership - Brand, Medical & Access
  • DS&AI business partner and product owner for assigned tumor-area brand teams, Medical Affairs, and Market Access - attending strategy reviews, launches, and leadership committee meetings as the AI expert at the table.
  • Lead cross-functional discovery workshops that reveal unmet decision needs. Develop well-scoped AI use cases using detailed problem descriptions, success metrics, data requirements, and risk tiers. Convert the results into actionable user stories for engineering teams.
  • Partner with MSL and medical leadership on scientific use cases: treatment-pathway analytics, KOL/KEE influence mapping (Cami constellation), diagnostics/biomarker testing strategies and health equity analytics.
  • Own and govern demand-sensing and care-gap workstreams feeding FSIP, forecasting, and field strategy plans; present roadmap and field impact at leadership team meetings, national sales meetings, and SteerCo forums.

4. Capability Building & Change Management
  • Set the direction for how OBU develops AI functions in Sales, Marketing, and Medical Affairs. Prioritize solutions that meet key business needs. Ensure these solutions are scalable, balanced, and embedded in daily field workflows.
  • Identify the people and process changes required to successfully stand up each AI capability; act as change leader, delivering the organizational AI transformation narrative to sales and marketing teams.
  • Partner with Business Excellence and Franchise teams to design and execute training plans, field enablement workshops, and persona-aligned onboarding for every major capability release.
  • Drive AI literacy across brand, medical, and field teams through capability reviews, lunch-and-learns, and executive presentations; build self-service fluency with AZ AI platforms.

5. Execution Excellence & Delivery Accountability
  • Ensure accurate and timely translation of business needs into technical requirements. Partner with the AI division passionate about enterprise solutions in data science and engineering. Establish clear RACI and maintain alignment on priorities, dependencies, and timelines across the full delivery model.
  • Drive launch-critical AI results on time: pre-launch analytics, ML-enriched targeting lists, EHR/claims/lab-signal predictive triggers per AIDLC framework.
  • Supervise progress and surface interdependencies across the product portfolio; creatively address key challenges and blockers and escalate to senior leadership with clear problem framing and proposed solutions.
  • Manage external vendors and consulting partners against defined deliverables and timelines; support budget management and PMO invoicing.

6. AI Innovation, Data Science & Emerging Capabilities
  • Identify and pilot emerging AI/ML techniques for the OBU portfolio: LLM-powered field tools, agentic frameworks, biomarker-signal models, multimodal data fusion (structured claims + unstructured clinical notes + lab signals), and RWE analytics.
  • Lead or co-lead data-source evaluations; articulate granularity caveats and preprocessing requirements to both technical and business audiences.
  • Design and implement the OBU predictive ML trigger ecosystem for earlier patient identification across indications, with reusable frameworks that scale across tumor areas.
  • Drive precision-medicine and earlier-patient-identification strategies: design AI trigger frameworks that detect high-risk patients upstream of clinical decision points, reducing diagnostic and treatment lag.

7. AI Governance, Compliance & Data Stewardship
  • Operate within AstraZeneca's enterprise AI governance framework: registry-before-scale, model and data cards, human-in-the-loop oversight, ongoing monitoring, and IT approval processes.
  • Navigate compliance requirements, data access controls, HIPAA privacy guardrails, and vendor management for all OBU AI use cases; ensure all products are deployed responsibly with appropriate documentation.
  • Partner with OBEX, FSIP, Legal, Privacy, and I&A to ensure AI-ready data assets (enriched claims, EHR, MMIT) are compliant, auditable, and operationalized with standardized QA and refresh cadences.
  • Maintain auditable AIDLC; govern UAT 1 compliance approval 1 release gates with scenario-based stakeholder demonstrations and documented acceptance criteria.

8. Impact Measurement & Value Tracking
  • Define success criteria and KPIs before launch; supervise adoption metrics (app utilization, trigger execution rate, noise reduction, coverage), output quality, and downstream business impact (sales lift, patient-identification speed, care-gap closure) post-launch.
  • Lead causal-impact assessments to quantify commercial lift attributable to triggers and AI applications; use findings to guide cross-OBU collaboration and investment decisions.
  • Communicate results to OBU and enterprise leadership with clarity - translating product performance data into business impact narratives that reinforce AI investment decisions and advise future roadmap prioritization.

Essential Requirements
  • Bachelor's degree or comparable experience in a quantitative field like CS, statistics, engineering, economics, or similar. Advanced qualifications, including an MBA, MS, PhD, or equivalent experience, are highly valued.
  • 7-10+ years of professional experience with 5+ years in pharma commercial functions (or pharma-focused strategy consulting); demonstrated end-to-end AI/data-science product ownership with measurable commercial impact in an oncology or specialty context.
  • Strong analytical fluency - able to translate between brand strategy, commercial operations, data science, and technology teams; experienced at bridging business and technical stakeholders across a highly matrixed organization.
  • Hands-on familiarity with LLMs, crafting input queries, retrieval-augmented generation systems, and agentic frameworks (LangChain, LlamaIndex, or equivalent); working proficiency in Python and ML/AI tooling; experience with cloud platforms (AWS SageMaker/Athena, Databricks, Azure, or GCP).
  • Deep expertise in oncology commercial data: secondary claims (IQVIA, Symphony, MMIT), EHR/EMR sources such as Komodo and Veeva PULSE, lab/diagnostic data (Diaceutics), specialty pharmacy, and enriched ML assets.
  • Exceptional communication skills and proven track record to operate successfully in highly matrixed organizations; effective interpersonal and relationship-management skills with a consultative approach.
  • Strong initiative: ability to understand core business goals, set direction, build alignment, and drive work forward proactively; resourceful and influential in a fast-growing organization.

Preferred Qualifications
  • Graduate degree (MBA, MS, MD, or PhD) in a quantitative, scientific, or business discipline.
  • Demonstrated commercial experience in Oncology
  • Production GenAI / agentic AI experience in commercial pharma: LLM-powered field tools, RAG-based clinical applications, or agentic omnichannel workflow automation under enterprise governance.
  • Experience with KOL/KEE influence analytics, precision-targeting tools (e.g., EGFR-testing waypoint analysis), or omnichannel NBA platforms (Aktana, Veeva Align).
  • Track record of leading AI-enabled business transformation and change management programs across large organizations (1,000+ users).
  • External thought leadership: conference presentations or publications in oncology analytics or commercial AI

Technical competency profile
Ideal candidates will demonstrate applied depth across:

GenAI & Agentic AI
  • LLMs and prompt engineering: GPT-4 / Claude / Gemini family; system prompting, few-shot, chain-of-thought, structured output
  • RAG architectures: vector stores, chunking strategies, retrieval evaluation, hallucination mitigation
  • LLMOps: evaluation pipelines, output quality monitoring, A/B testing for GenAI applications
Classical ML & Data Science
  • Supervised/unsupervised learning: logistic regression, gradient boosting (XGBoost/LightGBM), clustering, propensity scoring for patient/HCP targeting
  • Time-series and sequence models for patient-journey stage prediction and trigger timing optimization
  • Causal inference and test-and-control methodologies for AI impa

About AstraZeneca

AstraZeneca is a British-Swedish multinational pharmaceutical company that specializes in the research, development, and manufacturing of prescription drugs. The company was formed in 1999 through the merger of Astra AB and Zeneca Group plc. AstraZeneca's products are used to treat a wide range of medical conditions, including cancer, cardiovascular disease, respiratory disease, and diabetes. The company has operations in over 100 countries and employs more than 76,000 people worldwide. AstraZeneca is committed to developing innovative medicines that improve the health and well-being of people around the world.
Learn more about AstraZeneca
Size
83,100 employees
Market Cap
$211.5 billion
Industry
Pharmaceuticals & Biotech
Net Income
$3.1 billion
Founded
1999
5 Year Trend
+10.2%
Revenue
$26.6 billion
NASDAQ
AZN
* Ladders Estimates

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