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Enterprise Account Executive (Financial Services)

On-site
Scale AISan Francisco, CA, US / New York, NY, US1 month agoWebsite
Fresh
Enterprise Sales

Compensation

$200,000-$230,000
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Description

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 emerging technology environments

Stack

LLMsAgentic AIMachine Learning
Posted
Apr 28, 2026
Last seen
Jun 25, 2026
First seen
Jun 25, 2026
Status
active