Jobs / United States / Pfizer INC
AI Oncology Portfolio Lead (Director)
Pfizer INC · 🇺🇸 United States - California - San Diego
Sponsorship verdict
Sponsorship possible
One solid signal, not two — worth applying, and worth asking about sponsorship early.
- Employer is on a government sponsor recordThe US Department of Labor certified 40 H-1B/E-3 labor condition applications for this employer between Oct 2025 and Jun 2026 (latest Jun 2026) — the step every H-1B hire needs first. USCIS also records 9 H-1B approvals in FY2023. Source: LCA disclosure data (US Department of Labor (OFLC)).
- The posting doesn’t mention sponsorshipSilence isn’t a refusal — ask the recruiter before investing much time.
- No salary bar for this routeH-1B has no fixed salary bar: the employer must pay at least the prevailing wage for the role and area. Cap-subject employers enter a lottery weighted by wage level. Source: https://www.federalregister.gov/documents/2025/12/29/2025-23853/weighted-selection-process-for-registrants-and-petitioners-seeking-to-file-cap-subject-h-1b, rules effective 2026-02-27.
- What Pfizer INC paid sponsored hires in similar roles1 certified filing for “Director, Global Regulatory Intelligence and Analysis” (Regulatory Affairs Specialists) in CA: $136k–$136k, median $136k. Most were filed at wage level IV (100%) — 4 lottery entries, ≈61% projected selection odds for cap-subject employers. Source: US Department of Labor LCA disclosure data (Oct 2025 – Jun 2026).
- Confirmed live todayWhen a source last listed this job as open.
US H-1B: cap-subject employers enter a lottery weighted by wage level — Level I gets 1 entry, Level IV gets 4 (DHS projected selection odds ≈15% at Level I to ≈61% at Level IV). Universities and non-profit research employers are cap-exempt. The $100,000 fee for new petitions from abroad is currently blocked by a court order (appeal pending).
A verdict summarises public evidence; it is not legal advice and never a guarantee — the employer and the immigration authority decide. Sign in to factor in where you can already work.
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Sponsor Radar — Pfizer INC
The US Department of Labor certified 40 H-1B/E-3 labor condition applications for this employer between Oct 2025 and Jun 2026 (latest Jun 2026) — the step every H-1B hire needs first. USCIS also records 9 H-1B approvals in FY2023. Source: LCA disclosure data (US Department of Labor (OFLC)).
Past sponsorship or register membership never guarantees sponsorship for this vacancy or for you. Full Sponsor Radar for Pfizer INC →
About the role
ROLE SUMMARY As an AI-enabled strategic scientific leader, you will partner with Oncology Research Leadership to integrate AI-driven strategies that will drive data-informed decision-making across our Oncology Research pipeline. As the AI Portfolio Lead, you will bridge cutting-edge AI/advanced analytics with deep Oncology R&D expertise to guide our portfolio strategy. Working as an individual contributor reporting into the Head, Portfolio Strategy and Program Management, you will analyze and continuously assess Oncology Research projects using AI-driven insights (predictive models, scenario simulations, competitive intelligence) and propose actionable strategies to Oncology Research Leadership. This role is science-focused and spans across discovery and preclinical stage programs, ensuring that our project teams pursue the most promising scientific approaches and mechanisms of action. Your work will directly inform pipeline prioritization and resource allocation decisions, enhancing the quality and objectivity of governance deliberations with robust data. ROLE RESPONSIBILITIES • Portfolio Analysis and Insights: Continuously analyze the oncology Research portfolio (ESD through Preclinical) using advanced AI/ML tools and analytics to evaluate each program’s scientific strength, probability of success, and strategic portfolio fit per disease area strategies. • Strategy Recommendation: Develop and propose data-driven portfolio strategies (at both project and portfolio levels) to senior leaders and Research governance committee. Use scenario analysis and predictive modeling to highlight optimal project prioritization, pipeline balance, and resource allocation scenarios. • AI-Enabled Decision Support: Integrate AI-derived insights (e.g. machine learning predictions, knowledge graphs including gaps, competitive landscape mining) into project team and governance discussions, ensuring decisions are based on comprehensive evidence and highlighting opportunities to pivot or accelerate programs. • Cross-Functional Collaboration: Collaborate closely with discovery research leaders, translational scientists, clinical development teams, and business/strategy functions to synthesize cross-domain data (experimental results, clinical data, external trends) into unified portfolio recommendations. Serve as a key liaison bringing data-backed perspectives to portfolio governance forums. • Thought Leadership and Communication: Act as the subject matter expert on AI in R&D strategy, championing adoption of advanced analytics and decision intelligence tools. • Prepare and present high-impact reports (dashboards, visualizations, executive presentations) that distill complex analyses into clear, actionable insights for senior stakeholders. • Prepare AI-enabled project strategy recommendations at the Governance meetings to guide milestone decisions. • Continuous Improvement: Stay current with emerging AI technologies, industry best practices in portfolio management leveraging AI, and oncology scientific developments. Drive continuous improvement of our decision-making processes by identifying AI-driven new data sources or analytical approaches that enhance portfolio evaluation and forecasting accuracy. MINIMUM QUALIFICATIONS • PhD in a relevant scientific field (e.g. oncology, molecular biology, pharmacology, computational biology) and 5+ years of experience using AI to solve drug discovery problems, perform scenario modeling / simulation, and data visualization • Experience working with AI/ML teams or implementing AI tools in R&D processes • Experience interpreting and leveraging complex data models to drive decisions. • Strong understanding of Oncology R&D workflows and the role of data and AI in scientific and clinical decision making • 1