Principal (Senior Director), Commercial Enterprise AI Product Mgmt., Field Force Effectiveness

GSK • Boston, MA

3d • $177,750 - $325,875

About The Position

GSK’s Enterprise AI team translates AI into measurable impact across major business groups. This senior individual contributor will guide a portfolio of AI solutions for global field force effectiveness and own the forward-deployed strategy executed by each pod. The role identifies where AI can create value, develops prototypes, prioritizes opportunities, and helps ensure engineering resources are focused on the highest-impact work. It does not manage engineers; instead, it shapes what they build, where they focus, and how outcomes are measured.

The position combines product strategy, opportunity assessment, hands-on AI prototyping, business-case development, and executive alignment. It partners with forward-deployed, full-stack, and data engineering leads to turn ambiguous business needs into actionable problem statements, prototypes, success metrics, and decision frameworks. The role also shapes engagements from scope through scaling, identifies reusable platform capabilities, and designs the appropriate balance between digital workers, engineers, and business users with suitable evaluation and oversight.

The successful candidate will work with senior business and scientific leaders, build sponsor coalitions, communicate AI impact to executives, establish value-realization practices, and use post-mortems to improve future opportunity selection and engagement design. The role focuses on field force workflows such as rep coaching and enablement, omnichannel orchestration, field analytics, customer/HCP segmentation, territory alignment, and call plan execution.

Requirements

• At least 15 years of professional experience across some combination of pharma/life sciences, AI/ML product strategy, and product management or strategy roles in technology-led organizations.
• Experience taking at least one technology or AI initiative from problem framing through executive funding and into delivery.
• Prior responsibility for pharma field force operations, including rep coaching and enablement, omnichannel orchestration, or field analytics.
• Track record with modern AI/ML concepts, including LLMs, agentic systems, digital workers, the ML lifecycle, and data and evaluation patterns, demonstrated through hands-on prototyping.
• Experience as an individual contributor without direct reports, influencing engineering and business teams through frameworks and documented decision criteria.
• Experience in product management or product strategy, including opportunity sizing, roadmap development, prototyping, and stakeholder alignment.
• Regular use of field deployment processes, including customer/HCP segmentation, territory alignment, or call plan execution.
• Experience with field analytics and SFE KPI automation, including identifying execution gaps and producing actionable recommendations for the field.
• Proficiency with account planning or CRM systems such as Veeva or Salesforce to execute and track field plans.
• Experience partnering with sales leadership and brand teams to translate field performance data into targeting or resourcing decisions.
• Working knowledge of MLR and GxP compliance requirements applicable to field-facing recommendations and digital tools.

Nice To Haves

• Prior experience in a forward-deployed engineering, applied AI, or AI lab/COE role at a pharma, healthcare, or comparable regulated enterprise.
• Background in management consulting, value engineering, or corporate strategy combined with hands-on technology experience.
• Experience navigating GxP, regulatory, and data-governance requirements in pharma AI deployments.
• Experience with CRM, content, and data platforms used in field operations, such as Veeva CRM/Vault, Salesforce, IQVIA/Komodo data, or omnichannel engagement platforms.

Responsibilities

• Identify and prioritize high-value AI opportunities across field force effectiveness, including rep coaching and enablement, omnichannel orchestration, and field analytics; develop a global AI product portfolio ranked by impact, feasibility, and time-to-value.
• Build rigorous business cases quantifying value such as cycle time saved, faster decisions, revenue uplift, cost avoided, and scientific productivity gained.
• Develop understanding of field force operations workflows to identify AI opportunities not yet articulated by the business.
• Maintain a current portfolio view of in-flight, queued, and de-prioritized opportunities, including the rationale for decisions.
• Develop decision frameworks, define success metrics, analyze trade-offs, and recommend how engineering resources should be allocated when business units compete for them.
• Decline opportunities that are unlikely to produce scalable outcomes, using evidence to protect engineering focus.
• Partner with forward-deployed, full-stack, and data engineering leads to translate ambiguous business problems into clear problem statements, prototypes, success metrics, and decision frameworks.
• Shape engagements end-to-end by defining scope, success criteria, exit conditions, and scaling pathways before kickoff, and remain engaged during execution.
• Identify opportunities to turn one-off solutions into reusable capabilities and work with engineering to make the case for platform reuse.
• Design the division of work between digital workers, engineers, and business users, including evaluation and oversight approaches.
• Lead executive conversations that turn broad ambitions into concrete, fundable AI engagements.
• Build sponsor coalitions and secure the support, data access, and change-management commitment needed for engineering deployments.
• Communicate impact to executive leadership and the board, connecting AI work to patients, science, and shareholder outcomes.
• Establish the value-realization framework used to measure impact across Enterprise AI deployments.
• Conduct post-mortems, capture lessons, and feed findings into opportunity selection and engagement design.
• Continuously refine the investment thesis for where AI is and is not appropriate at GSK.

Benefits

• Annual bonus
• Eligibility to participate in a share-based long-term incentive program, dependent on role level
• Health care and other insurance benefits for the employee and family
• Retirement benefits
• Paid holidays
• Vacation
• Paid caregiver/parental and medical leave

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed