Senior Manager, Applied Intelligence

ROLE SUMMARY

As a Senior Manager, Applied Intelligence, you will take ambiguous, high-value AI/ML problems in Pfizer's commercial business and turn them into working first versions. Successful prototypes become the foundation of systems that scale, so they are built to be understood, reproduced, and extended by teams who did not write them.

This is a hands-on individual contributor role with informal technical leadership: you mentor colleagues, set the technical bar for the work, and own the build and write the code, from framing an unclear problem into something answerable through to preparing it for the team that takes it forward. The role sits within Pfizer's commercial data science organization and supports commercial use cases with global scope. The work serves Pfizer's commercial business, and each project is judged by the commercial value it delivers.

ROLE RESPONSIBILITIES

• Problem framing and scoping: Translate requests as they arrive into well-defined, answerable problems. Identify the underlying need, agree explicit success criteria with stakeholders that tie the work to a commercial outcome, determine what existing capabilities already address, and commit to an approach once the problem is understood well enough.
• Hands-on delivery: Own prototype development end to end, including experimental design, data pipelines, modeling, evaluation, and the surrounding engineering, writing the code personally. Deliver at a pace measured in weeks without skipping work that is expensive to add later. Own accountability for managing multiple concurrent projects and stakeholders, prioritizing across competing requests, setting realistic timelines, and escalating risks early.
• Methodological rigor: Convert ambiguous business questions into testable hypotheses validated through structured experimentation, with sound evaluation design, comparison against meaningful baselines, and metrics chosen to match the decision the model supports.
• Engineering judgment: Calibrate engineering rigor to the cost of being wrong now and the cost of fixing later. Apply full validation wherever errors carry real consequences, however early the project, and avoid hardening disposable work or building for scale no one has established.
• Reuse and handoff: Build so that solving a problem once makes the next problem of a similar shape cheaper. Judge when components have enough evidence to generalize, and deliver structured handoffs covering dependencies, which of them cannot change without discussion, and what was deliberately left unfinished. Success is the receiving team's ability to own and extend the work.
• Standards and shared assets: Codify what worked on your projects into reusable components, templates, and documented patterns for the team. Apply the team's engineering and MLOps standards, flag where project experience suggests they need to change, and document the reasoning behind hard-to-reverse decisions along with the conditions that would justify revisiting them.
• Technical leadership and mentoring: Provide informal technical leadership without direct reports. Mentor and coach colleagues, review code and experimental designs, and raise the technical standard of the team through example and constructive feedback.
• Communication: Be clear about what a result establishes and what it does not, raise shortcuts and known limitations when they arise, and translate technical outcomes into written guidance business stakeholders can act on without you in the room. Give an honest assessment of risk and trajectory, and provide the evidence and recommendations that inform go/no-go and investment decisions, linking results to their expected commercial impact, including recommending a change in direction early when one is warranted.

BASIC QUALIFICATIONS

• Bachelor's degree in Computer Science, Statistics, Mathematics, Computational Biology, Engineering, or a related quantitative field with 6+ years of relevant experience; OR a Master's degree with 5+ years of relevant experience; OR a PhD with 1+ year of relevant experience.
• Hands-on experience building AI/ML systems, with current technical proficiency in Python, Git, and modern ML frameworks (e.g., scikit-learn, PyTorch, TensorFlow).
• Experience taking work from exploration through to something other systems or scheduled jobs depend on.
• Rigorous approach to model evaluation and validation, including designing sound evaluations, selecting appropriate metrics, and being clear about what results do and do not support.
• Proven ability to work from an ambiguous problem statement, defining the right question rather than executing a specification someone else wrote.
• Strong written communication, including documentation that anotherengineer can act on without the author present.

 

PREFERRED QUALIFICATIONS

• 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 (LLMs), including prompt engineering, retrieval-augmented generation, and frameworks such as LangChain.
• Applied AI/ML in commercial functions in pharmaceutical or life sciences, for example forecasting, customer segmentation, pricing, or real-world evidence analytics.
• Working knowledge of commercial pharmaceutical data and how it informs 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 other than your 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: 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.

 
Candidate demonstrates a breadth of diverse leadership experiences and capabilities including: the ability to influence and collaborate with peers, develop and coach others, oversee and guide the work of other colleagues to achieve meaningful outcomes and create business impact.

Other Job Details:

• Last Date to Apply for Job: November 5, 2026.
• Ability to travel based on business needs
• This position is hybrid and requires working onsite 2 to 3 days per week
  

 

 

The annual base salary for this position ranges from $124,400.00 to $207,400.00. In addition, this position is eligible for participation in Pfizer’s Global Performance Plan with a bonus target of 17.5% of the base salary and eligibility to participate in our share based long term incentive program. We offer comprehensive and generous benefits and programs to help our colleagues lead healthy lives and to support each of life’s moments. Benefits offered include a 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, and health benefits to include medical, prescription drug, dental and vision coverage. Learn more at Pfizer Candidate Site – U.S. Benefits | (uscandidates.mypfizerbenefits.com). Pfizer compensation structures and benefit packages are aligned based on the location of hire. The United States salary range provided does not apply to Tampa, FL or any location outside of the United States.

 

 

 

 

 

 

This role is posted in multiple locations. If you are applying for the role in an secondary job posting location where pay transparency regulations apply, your Talent Advisor will share the local pay information with you during the first interview.

 

 

Relocation assistance may be available based on business needs and/or eligibility.

 

 

Candidates must be authorized to be employed in the U.S. by any employer.

U.S. work visa sponsorship (such as TN, O-1, H-1B, etc.) is not available for this role now or in the future.

 

 

 

 

 

 

To learn more about acceptable and prohibited uses of AI during the recruitment process, please review our candidate AI-use guidelines available on Pfizer Careers.

 

 

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