Job description

We are seeking an Enterprise Account Executive to drive revenue growth for Stagwell’s agentic AI product business. This is a quota-carrying, full-cycle sales role for a hands-on enterprise seller. You will own opportunities from pipeline generation through close — running discovery, delivering product demos, building the business case, and driving multi-stakeholder deals across the finish line. The ideal candidate combines a proven track record in enterprise AI, SaaS, or data product sales with the technical fluency to engage credibly with both business buyers and technical evaluators. Because our engagements pair a software product with enterprise implementation, we are specifically looking for sellers who have sold both pure SaaS and software implementations — not one to the exclusion of the other. This is an individual contributor role, not a management position. Success is measured by pipeline generated, deals closed, and revenue delivered. WHAT YOU'LL DO Own and drive full-cycle enterprise sales opportunities — from prospecting and qualification through negotiation and close Build and maintain a healthy pipeline through outbound prospecting, inbound qualification, and partner-sourced opportunities Lead discovery sessions and technical qualification, mapping customer business challenges, data requirements, and success criteria to product capabilities Deliver compelling product demonstrations and configure tailored demos that connect agentic AI capabilities to measurable business outcomes Translate customer needs into product-led use cases, articulating clear ROI and value narratives Navigate complex, multi-stakeholder buying committees — engaging economic buyers, technical evaluators, and executive sponsors Develop high-quality sales assets including presentations, business cases, and pitch content Maintain accurate forecasting and pipeline hygiene in CRM; drive consistent deal momentum through structured follow-up Partner with product, marketing, and delivery teams to sharpen positioning, inform the roadmap with customer feedback, and ensure clean post-sale handoffs Stay current on the agentic AI landscape, competitive platforms, and enterprise adoption trends WHAT YOU'LL NEED Proven track record in enterprise product sales — AI, SaaS, data, or analytics platforms — with a history of quota attainment Experience selling both SaaS subscriptions and software implementations — comfortable structuring deals that combine product licensing with implementation and services scope (e.g., a Salesforce-style ecosystem background), not implementations-only Experience owning full-cycle sales, including prospecting, discovery, demo, business case, negotiation, and close Demonstrated success navigating complex, multi-stakeholder enterprise sales cycles with 6– to 7-figure deal sizes Strong command of product demos and value-based selling — able to run technical qualification without leaning on a sales engineer for every conversation Technical fluency in AI, data, analytics, or platform architecture sufficient to engage engineers and architects credibly Excellent presentation, communication, and executive storytelling skills Self-directed pipeline builder who thrives with autonomy while collaborating in a team-based go-to-market motion NICE TO HAVE Experience selling agentic AI, machine learning, or GenAI products into the enterprise Familiarity with a structured sales methodology (e.g., MEDDICC, Challenger, value selling) Background in sales engineering or solution consulting that evolved into a closing role Background at a platform company such as Salesforce, Adobe, or a major data/cloud vendor where deals combined subscription software with implementation scope Exposure to marketing, media, audience, or customer experience technology environments ABOUT US Born in 2001, Code and Theory is a digital-first creative agency that sits at the center of creativity and technology. We pride ourselves on not only solving consumer and business problems, but also helping to establish new capabilities for our clients. With a global client roster of Fortune 100s and start-ups alike, we crave the hardest problems to solve. We have teams distributed across North America, South America, Europe, and Asia. The Code and Theory global network of agencies is growing and includes Kettle, Instrument, Left Field Labs, Create Group, Current, and TrueLogic. Striving never to be pigeonholed, we work across every major category: from tech to CPG, financial services to travel & hospitality, government and education to media and publishing. We value the collaboration with our client partners, including but not limited to Adidas, Amazon, Con Edison, Diageo, EY, J.P. Morgan Chase, Lenovo, Marriott, Mars, Microsoft, Thomson Reuters, and TikTok. The Code and Theory network is comprised of nearly 2,000 people with 50% engineers and 50% creative talent. We’re always on the lookout for smart, driven, and forward-thinking people to join our team. The target range of base compensation for this role is $150,000 - $200,000. Actual compensation is influenced by a wide array of factors including but not limited to skill set, level of experience, and location.

Requirements

Must-haves

We are seeking an Enterprise Account Executive to drive revenue growth for Stagwell’s agentic AI product business.
This is an individual contributor role, not a management position. Success is measured by pipeline generated, deals closed, and revenue delivered.
Own and drive full-cycle enterprise sales opportunities — from prospecting and qualification through negotiation and close
Build and maintain a healthy pipeline through outbound prospecting, inbound qualification, and partner-sourced opportunities
Lead discovery sessions and technical qualification, mapping customer business challenges, data requirements, and success criteria to product capabilities
Deliver compelling product demonstrations and configure tailored demos that connect agentic AI capabilities to measurable business outcomes
Translate customer needs into product-led use cases, articulating clear ROI and value narratives
Navigate complex, multi-stakeholder buying committees — engaging economic buyers, technical evaluators, and executive sponsors
Develop high-quality sales assets including presentations, business cases, and pitch content
Maintain accurate forecasting and pipeline hygiene in CRM; drive consistent deal momentum through structured follow-up
Partner with product, marketing, and delivery teams to sharpen positioning, inform the roadmap with customer feedback, and ensure clean post-sale handoffs
Stay current on the agentic AI landscape, competitive platforms, and enterprise adoption trends
Proven track record in enterprise product sales — AI, SaaS, data, or analytics platforms — with a history of quota attainment
Experience owning full-cycle sales, including prospecting, discovery, demo, business case, negotiation, and close
Demonstrated success navigating complex, multi-stakeholder enterprise sales cycles with 6– to 7-figure deal sizes
Strong command of product demos and value-based selling — able to run technical qualification without leaning on a sales engineer for every conversation
Technical fluency in AI, data, analytics, or platform architecture sufficient to engage engineers and architects credibly
Excellent presentation, communication, and executive storytelling skills
Self-directed pipeline builder who thrives with autonomy while collaborating in a team-based go-to-market motion

Nice-to-haves

Experience selling agentic AI, machine learning, or GenAI products into the enterprise
Familiarity with a structured sales methodology (e.g., MEDDICC, Challenger, value selling)
Background in sales engineering or solution consulting that evolved into a closing role
Background at a platform company such as Salesforce, Adobe, or a major data/cloud vendor where deals combined subscription software with implementation scope
Exposure to marketing, media, audience, or customer experience technology environments

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aigomachine learning

Platform: greenhouse
Effort: quick