Senior Manager, Scientific AI Engineer

Scientific Ai Engineer

Pfizer Oncology is building an AI-first R&D engine where artificial intelligence is a foundational capability shaping how medicines are discovered, developed, and delivered to patients. We are seeking a Scientific AI Engineer to design and build end-to-end AI solutions that directly impact Oncology R&D decision-making. This role sits at the intersection of deep scientific understanding and hands on AI engineering, translating complex biological, translational, and clinical questions into applied AI solutions. In this role, you will partner closely with Oncology scientists, clinicians, and product leaders to prototype, iterate, and deliver AI enabled insights, with a strong emphasis on speed, scientific rigor, and real-world usability. Key Responsibilities

Design, develop, and prototype AI/ML solutions addressing Oncology discovery, translational, and clinical development challenges. Apply advanced analytical and machine learning methods to multimodal datasets (e.g., molecular, clinical, real-world, literature). Own solutions end-to-end, from problem framing and data exploration through model development and user facing outputs. Collaborate closely with domain experts to ensure solutions are scientifically grounded and decision relevant. Rapidly iterate on prototypes based on user feedback and evolving scientific needs. Contribute technical expertise to solution design discussions led by the Oncology AI Product & Engineering Lead. Document methods, assumptions, and limitations to support transparency and responsible AI practices. Basic Qualifications

Bachelor's degree and 6+ years of relevant work experience OR Master's degree and 5+ years of experience OR PhD and 1+ years of experience. Advanced degree in computational biology, data science, machine learning, engineering, or related field strongly preferred. Demonstrated experience building applied AI/ML solutions in life sciences, healthcare, or advanced analytics environments. Strong hands-on programming skills (e.g., Python) and experience working with data pipelines and ML frameworks. Solid understanding of Oncology biology, translational science, or clinical development workflows. Ability to operate independently in ambiguous problem spaces and deliver working prototypes. Preferred Qualifications

Experience in pharma, biotech, or AI driven health technology startups. Familiarity with prototyping approaches for AI products rather than long cycle production systems. Experience working with large, heterogeneous datasets common to Oncology R&D.