opengreenhouseremotely
Enterprise Account Executive (Financial Services)
Scale AI
LocationNew York, NY; San Francisco, CA, San Francisco, CA
Last observed2026-06-13 05:25:40.118785
Job idremotely-scale-ai:greenhouse:4689917005
The Enterprise Account Executive (Financial Services) will report to the Director of Enterprise GTM and own revenue growth across a portfolio of Scale AI’s most strategic financial services customers and prospects. This role is focused on selling complex, agentic AI solutions -autonomous workflows powered by LLMs and human-in-the-loop systems - into large banks, insurers, asset managers, and fintechs. You will operate as a strategic partner to senior executives across the business, technology, and risk organizations - helping them reimagine core workflows (e.g., underwriting, fraud detection, KYC, claims, research, and operations) through AI agents. This is a highly consultative, technical enterprise sales role requiring deep domain fluency, executive presence, and the ability to navigate regulatory, security, and multi-stakeholder complexity. You will own the full customer lifecycle - from origination through close, deployment, and expansion - while acting as the quarterback across Solutions Engineering, Product, Research, and Delivery teams to land and scale high-impact AI programs. You Will: Own and expand relationships with the largest financial services institutions (banks, insurers, capital markets, fintech), focusing on high-impact, multi-year AI transformations Sell agentic AI solutions by mapping Scale’s capabilities to mission-critical workflows (e.g., underwriting, fraud, compliance, customer ops, investment research) Build trusted relationships with executive stakeholders (CIO, CTO, Chief Data/AI Officer, Heads of Risk/Operations/Lines of Business) and guide enterprise AI strategy Develop and execute multi-threaded account plans that drive net-new revenue, expansion, and long-term platform adoption Lead complex deal cycles, including business case development, ROI modeling, and mutual close plans across new business, renewals, and expansions Partner deeply with Solutions Engineering to shape and land technically credible pilots, POVs, and production deployments Navigate regulatory, security, and procurement processes unique to financial services environments Act as the voice of the customer internally—informing product roadmap, agent design, and vertical-specific solutions Maintain a strong command of pipeline, forecasting, and deal hygiene using Salesforce, Clari, and related tools Operate with urgency and precision in a fast-paced, highly cross-functional environment Ideally, You Will Have: 8–12+ years of enterprise sales experience, with significant focus on financial services (banking, insurance, capital markets, or fintech) Proven track record of closing and expanding large, complex, multi-million dollar enterprise deals within highly regulated environments Experience selling AI/ML, data platforms, or workflow automation technologies, with the ability to position agentic / LLM-driven solutions at a business and technical level Deep understanding of financial services workflows (e.g., underwriting, claims, fraud, KYC/AML, research, operations) and how technology transforms them Demonstrated success engaging and influencing executive stakeholders, including building and delivering compelling business cases and ROI narratives Strong command of enterprise sales methodology, including account planning, multi-threading, and disciplined forecasting Ability to navigate ambiguity, long sales cycles, and complex stakeholder landscapes with high ownership and resilience Excellent communication and storytelling skills across both technical and non-technical audiences High level of business acumen, technical curiosity, and a consultative, customer-first mindset Nice to Haves: Experience selling agentic AI, LLM platforms, or automation solutions into financial services Background working with or alongside risk, compliance, or data organizations within large enterprises Familiarity with regulatory considerations (e.g., model risk management, auditability, data privacy) in AI deployments Prior experience in high-growth or eme
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