Senior Program Manager (Life Sciences Discovery) at Microsoft

Redmond, Washington, United States -

Full Time

Start Date

Immediate

Expiry Date

09 Mar, 26

Salary

234700.0

Posted On

09 Dec, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Biology, Drug Discovery, AI/ML, Data Analytics, Project Management, Cloud Computing, Collaboration, Life Sciences, Pharmaceuticals, Computational Biology, Cheminformatics, Bioinformatics, Lab Automation, Customer Engagement, Partnership Development, Technical Communication

Industry

Software Development

Description

Overview Microsoft Discovery is a new enterprise Artificial Intelligence (AI) platform built on Azure to accelerate scientific Research and Development (R&D) with agentic AI – enabling researchers to collaborate with specialized AI agents and a graph-based knowledge engine for faster, smarter discovery. We are seeking a Senior Program Manager (Life Sciences Discovery) who will serves as a Life Sciences & Pharma R&D specilist to join the Microsoft Discovery product team. In this role, you will leverage deep domain expertise in biology and drug discovery to drive successful use cases on the platform, integrate scientific models and tools, and expand our life sciences partner ecosystem. You will collaborate closely with product engineering, research teams, customers, and industry partners to transform how Biotech, Pharma and Contract Research Organizations (CROs), organizations innovate using Microsoft’s agentic AI technology. This is a high-impact individual contributor role, well-suited for someone passionate about connecting cutting-edge AI with real-world life sciences R&D. In alignment with our Microsoft values, we are committed to cultivating an inclusive work environment for all employees to positively impact our culture every day. Microsoft’s mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and to create a culture of inclusion where everyone can thrive at work and beyond. Responsibilities Key Responsibilities: Use Case Development: Engage directly with pharmaceutical and biotech researchers to identify high-impact AI R&D use cases for Microsoft Discovery. Guide these projects from ideation through prototyping and deployment, demonstrating how agentic AI workflows (orchestrated AI agents with tools/models) can accelerate hypotheses generation, experimentation, and analysis. Customer Onboarding & Training: Lead the onboarding of new life sciences customers onto the Discovery platform. Ensure researchers can effectively use Discovery’s capabilities (e.g. specialized agents, Azure High-Performance Computing (HPC) simulations) to solve their scientific challenges. Converting early wins into scalable playbooks and field adoption pattern. Agent & Model Integration: Collaborate with Microsoft Discovery engineers and data scientists to integrate domain-specific models, tools, and data sources into the platform. For example, help incorporate cheminformatics libraries, bioinformatics pipelines, lab automation tools, or proprietary models into Discovery’s catalog. Define specialized AI agents and workflows that capture life sciences experimental processes and reasoning, enhancing the platform’s ability to handle complex biological data and simulations. Partner Ecosystem Expansion: Identify and onboard strategic life sciences R&D partners – such as research institutions, biotech startups, ISVs, or data providers – whose tools or expertise can extend the Discovery platform’s capabilities. Work with these partners to bring their data, models or IP onto Azure and integrate with our graph-based knowledge engine, creating a rich ecosystem of domain solutions on Discovery. Domain Leadership & Advocacy: Serve as the internal advisor for biology and pharma R&D. Champion the needs of scientists – ensuring features like experiment traceability, data provenance, and collaborative analysis are prioritized for trustworthy AI-assisted research. Provide proactive insights on industry trends (e.g. lab automation, computational biology, AI in clinical trials) and regulatory considerations to inform product strategy. Cross-Team Collaboration: Work cross-functionally to drive scenario success. Partner with architects to shape the product roadmap with life sciences requirements. Team up with Microsoft Research, Azure HPC, and Health & Life Sciences engineering groups to co-develop new capabilities (e.g. novel agent algorithms, specialized foundation models) that address gaps in the drug discovery process. Coordinate with field teams to align on customer engagements and ensure feedback loops from preview deployments drive continuous improvement. Impact Tracking: Define success metrics for life-sciences scenarios (e.g. reduction in experiment cycle time, number of novel insights generated by AI) and track the impact of Discovery deployments at customer sites. Thought leadership: Represent Microsoft’s vision in the space across industry by contributing talks and presentation in industry and academic events. Other: Embody our Culture and Values Qualifications Required Qualifications: Bachelor's Degree in Biology, Bioinformatics, Chemistry, Computer Science (with life sciences focus) or related field AND 4+ years of hands-on R&D experience in life sciences or pharma with understanding of the end-to-end scientific research process – from literature review and hypothesis formulation to experimentation and data analysis – particularly in biology or pharmaceutical contexts. OR equivalent experience. 2+ years of experience managing complex, multi-stakeholder projects or programs, with proficient organizational skills to coordinate customer engagements, partner integrations, and internal development schedules, and a proven ability to solve problems, take initiative, and drive projects to successful completion. 3+ years of experience applying AI/ML or data analytics to scientific problems, with familiarity in modern AI approaches such as large language models, knowledge graphs, lab automation, and simulations, and the ability to clearly explain technical concepts, including multi-agent systems, model training, and cloud computing (Azure services, HPC) in the context of scientific research. Demonstrated entrepreneurial mindset, ownership, organization, system-level thinking to drive new products and iterate rapidly in ambiguous environments; adept at collaborating with customers and cross-disciplinary teams to bridge scientists and engineers, with competent communication skills for leading training sessions, presenting technical concepts to non-experts, and influencing decision-makers. Experience building partnerships or managing vendor relationships in the biotech/pharma R&D ecosystem, with a background in facilitating collaboration between technology teams and scientific stakeholders, such as in technology consulting for pharma or serving as a liaison between IT and R&D labs. Other Requirements: Ability to meet Microsoft, customer and/or government security screening requirements are required for this role. These requirements include, but are not limited to the following specialized security screenings: Microsoft Cloud Background Check: This position will be required to pass the Microsoft Cloud Background Check upon hire/transfer and every two years thereafter. Preferred Qualifications: Agentic AI & Workflows: Experience with AI agents or dynamic workflow orchestration in any context (e.g. robotic process automation, autonomous lab systems, or multi-agent simulations). Cloud and Platform Skills: Hands-on experience with cloud-based data or ML platforms (Azure preferred). Familiarity with deploying or managing scientific applications on Azure (Azure ML, Azure Batch or HPC, cognitive services) and knowledge of Azure data security/compliance for life sciences (e.g. handling of PHI/PII data). Life Sciences Computing: Knowledge of common computational biology or chemistry tools (for example: molecular modeling software, bioinformatics pipelines, LIMS, ELN systems, etc.). Ability to assess and integrate such tools’ APIs or models into larger platforms. Publications/Patents: Track record of publications in relevant scientific or technical journals, or patents in AI for life sciences, demonstrating thought leadership in applying innovative technology to research. Technical Program Management IC4 - The typical base pay range for this role across the U.S. is USD $119,800 - $234,700 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $158,400 - $258,000 per year. Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here: https://careers.microsoft.com/us/en/us-corporate-pay This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled. Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, citizenship, color, family or medical care leave, gender identity or expression, genetic information, immigration status, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran or military status, race, ethnicity, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance with religious accommodations and/or a reasonable accommodation due to a disability during the application process, read more about requesting accommodations.

Responsibilities

The Senior Program Manager will engage with researchers to identify AI R&D use cases, guide projects from ideation to deployment, and lead customer onboarding. They will also collaborate with engineering teams to integrate scientific models and expand the life sciences partner ecosystem.