Director, Oncology Commercial Data Science & AI Products

We're building a connected, end-to-end Enterprise AI engine - uniting data foundations, AI technology, process reinvention, and business-facing AI to accelerate results across the whole value chain. Success depends on being exceptional connectors: you'll actively leverage existing capabilities, celebrate and promote reuse, export breakthrough ideas across geographies and functions, and obsess over scaling impact rather than building in isolation. If you thrive in high-collaboration environments where your role is to turn complex, cross-functional problems into reusable, enterprise-wide capabilities - and where the measure of success is adoption and scale, not just innovation - you'll have the platform (and sponsorship) to make it real.  

Our DS&AI Products team is the OBU's in-house engine for that work. We lead all aspects of the full lifecycle of AI-powered applications spanning predictive field triggers, patient-identification models, omnichannel next-best-action, KOL intelligence, and GenAI-powered field tools — and we ship products, not reports. Every capability we build must deliver measurable field adoption and commercial impact.  

We are hiring a Director, DS&AI Products who will lead a multi-indication AI product portfolio end-to-end, serve as the strategic DS&AI business partner to OBU brand and medical leadership, and lead the responsible design, delivery, adoption, and impact measurement of AI solutions that drive commercial lift and better patient outcomes.  

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 → compliance approval → 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 impact attribution and commercial ROI measurement  

Data & Cloud Platforms 

• Oncology data: IQVIA (NPA, XPONENT, DDD), Symphony, MMIT, SaleSEER, Komodo, Veeva PULSE, Diaceutics, Definitive Health  

• Cloud: AWS (S3, Athena, SageMaker, Bedrock), Databricks, Snowflake; languages: Python, SQL, R, SAS  

• BI & visualization: Tableau, Power BI, Alteryx  

Product & Delivery 

• Agile/scrum: backlog grooming, sprint planning, user story writing, acceptance criteria, UAT management, release approval  

• AI governance: model/data cards, registry workflows, compliance documentation, evidence frameworks  

• UX collaboration: wireframing, user research, persona development, prototype feedback cycles with field and HQ teams  

• Omnichannel integration: trigger-code design, message alignment, field activation workflow. 

Office Working Requirements 

When we put unexpected teams in the same room, we unleash bold thinking with the power to inspire life-changing medicines. In-person working gives us the platform we need to connect, work at pace and challenge perceptions. That’s why we work, on average, a minimum of three days per week from the office. But that doesn’t mean we’re not flexible. We balance the expectation of being in the office while respecting individual flexibility. Join us in our unique and ambitious world.  

The annual base pay (or hourly rate of compensation) for this position ranges from $186.014,40 - 279.021,60 USD Annual, either as annual base pay or as the hourly rate (annual base pay divided by 2080 hours)]. Hourly and salaried non-exempt employees will also be paid overtime pay when working qualifying overtime hours. Base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. In addition, our positions offer a short-term incentive bonus opportunity; eligibility to participate in our equity-based long-term incentive program (salaried roles), to receive a retirement contribution (hourly roles), and commission payment eligibility (sales roles). Benefits offered included a qualified retirement program [401(k) plan]; paid vacation and holidays; paid leaves; and, health benefits including medical, prescription drug, dental, and vision coverage in accordance with the terms and conditions of the applicable plans. Additional details of participation in these benefit plans will be provided if an employee receives an offer of employment. If hired, employee will be in an “at-will position” and the Company reserves the right to modify base pay (as well as any other discretionary payment or compensation program) at any time, including for reasons related to individual performance, Company or individual department/team performance, and market factors. 

Ready to make a difference? Apply now!  

#EAI 

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