Senior Manager, Applied Intelligence

Pfizer • New York City, NY

2d • Hybrid

About The Position

The Senior Manager, Applied Intelligence is a hands-on individual contributor in Pfizer’s commercial data science organization, supporting commercial use cases with global scope. The role turns ambiguous, high-value AI/ML problems into working first versions that can be understood, reproduced, extended, and handed off to teams that did not build them. The position owns prototype development and coding end to end, from problem framing through experimentation, data pipelines, modeling, evaluation, engineering, and handoff. It also provides informal technical leadership through mentoring, code and experiment review, and setting a high technical bar, without direct reports. Projects are evaluated by their commercial value and the role provides evidence and recommendations that inform go/no-go and investment decisions.

The role includes defining business problems and success criteria with stakeholders, applying rigorous experimentation and model evaluation, calibrating engineering effort to project risks, and creating reusable components, templates, and documented patterns. It requires clear communication about results, limitations, risks, and commercial implications, as well as structured handoffs that enable receiving teams to own and extend the work.

This is a hybrid position requiring onsite work 2 to 3 days per week, with travel based on business needs. The annual base salary range is $124,400 to $207,400, with eligibility for a 17.5% bonus target and a share-based long-term incentive program. Relocation assistance may be available based on business needs and/or eligibility. Candidates must be authorized to work in the U.S.; visa sponsorship is not available.

Requirements

• Bachelor’s degree in Computer Science, Statistics, Mathematics, Computational Biology, Engineering, or a related quantitative field and 6+ years of relevant experience; or a master’s degree and 5+ years; or a PhD and 1+ year.
• Hands-on experience building AI/ML systems and current technical proficiency in Python, Git, and modern ML frameworks such as scikit-learn, PyTorch, or TensorFlow.
• Experience taking work from exploration through to something other systems or scheduled jobs depend on.
• Rigorous model evaluation and validation skills, including designing sound evaluations, selecting appropriate metrics, and accurately characterizing what results do and do not support.
• Ability to work from ambiguous problem statements and define the right question rather than simply execute a specification.
• Strong written communication, including documentation another engineer can act on without the author present.
• Ability to travel based on business needs.
• Must be authorized to work in the United States by any employer; permanent U.S. work authorization is required. U.S. work visa sponsorship is unavailable.

Nice To Haves

• Advanced degree (MS or PhD) in Computer Science, Statistics, Mathematics, Computational Biology, Engineering, or a related quantitative field.
• Experience building applications on large language models, including prompt engineering, retrieval-augmented generation, and frameworks such as LangChain.
• Applied AI/ML experience in pharmaceutical or life sciences commercial functions, such as forecasting, customer segmentation, pricing, or real-world evidence analytics.
• Working knowledge of commercial pharmaceutical data and its use in commercial decisions.
• Experience working in or with regulated industries such as pharmaceutical, biotech, medical devices, or financial services.
• Experience building shared components used by teams beyond one’s own, or transitioning a system to a team that did not build it.
• Experience authoring or contributing to internal engineering standards or best practices.
• Command of software engineering practices, including Git-based workflows and code review, CI/CD, dependency and environment management, automated testing, and reproducibility tooling.
• Experience with cloud-based machine learning platforms and enterprise data architectures, including deployment, monitoring, and data governance in regulated environments.
• Breadth of leadership experience and capabilities, including influencing and collaborating with peers, developing and coaching others, and guiding colleagues’ work to achieve meaningful outcomes and business impact.

Responsibilities

• Translate incoming requests into well-defined, answerable problems; identify underlying needs and existing capabilities, and agree with stakeholders on success criteria tied to commercial outcomes.
• Own end-to-end prototype development, including experimental design, data pipelines, modeling, evaluation, and engineering, personally writing the code.
• Manage multiple concurrent projects and stakeholders, prioritize competing requests, set realistic timelines, and escalate risks early.
• Convert ambiguous business questions into testable hypotheses and validate them through structured experimentation, sound evaluation design, meaningful baselines, and appropriate metrics.
• Calibrate engineering rigor to the cost of errors and future fixes; fully validate work where errors carry real consequences and avoid unnecessary hardening or scaling.
• Build reusable solutions where evidence supports generalization and provide structured handoffs covering dependencies, constraints, and unfinished work so receiving teams can own and extend the work.
• Create reusable components, templates, and documented patterns; apply team engineering and MLOps standards, identify where they may need to change, and document hard-to-reverse decisions and conditions for revisiting them.
• Mentor and coach colleagues, review code and experimental designs, and raise the team’s technical standard through example and constructive feedback.
• Communicate clearly what results establish and do not establish, disclose shortcuts and limitations, and translate technical outcomes into actionable written guidance for business stakeholders.
• Provide honest assessments of risk and trajectory, and evidence-based recommendations for go/no-go and investment decisions linked to expected commercial impact; recommend changes in direction when warranted.

Benefits

• 401(k) plan with Pfizer Matching Contributions and an additional Pfizer Retirement Savings Contribution.
• Paid vacation, holiday, and personal days.
• Paid caregiver/parental and medical leave.
• Medical, prescription drug, dental, and vision coverage.
• Eligibility to participate in Pfizer’s Global Performance Plan with a bonus target of 17.5% of base salary.
• Eligibility to participate in a share-based long-term incentive program.
• Relocation assistance may be available based on business needs and/or eligibility.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior