Mid Market Account Executive, SLED (North Central) - Remote

Overview

Abnormal AI is looking for an Account Executive to join the SLED team. This team sells our security solutions to accounts within a defined territory, with a specific focus on State, Local, and Education (SLED) agencies.

In Short

• Sell Abnormal security solutions to SLED agencies with the goal to overachieve new annual recurring revenue quota
• Work accounts from initial conversations through signing a contract and up-selling once they’re a customer
• Prospect and generate new business opportunities within accounts to supply enough pipeline for them to hit sales targets
• Work with Customer success to ensure a timely renewal and expansion sale opportunities
• Continually document results and maintain accurate data across all sales systems (Salesforce, Highspot, Close Plan)
• Be a voice for the customer/prospect with internal teams including Sales Engineering/POV team, Product and Marketing to ensure appropriate prioritization to close more revenue

Requirements

• SLED Account Hunter: Demonstrated 2+ years of direct (vs. overlay) sales experience prospecting and closing SLED accounts
• Proven track record of success closing new Account Logos, while also cross selling/ upselling and growing the existing customer account
• Skill in negotiating with large organizations and closing complex sales.
• Proven performer with consistent over quota performance and/or top 5% of sales org
• Technically competent: Conversant in key areas: security, email, cloud, AI, etc.
• Cyber-security Software sales: Experience selling subscription software/SaaS to CISOs and security personnel
• Start-up experience: Success at a company that was early stage, underdog or was a new entrant with large competitors or similar (limited resources-build territory including: channel, tech partners, initial customer wins, etc.)
• BS/BA degree or equivalent work experience

Benefits

• Eligible for a bonus, restricted stock units (RSUs), and benefits
• Individual compensation packages based on factors unique to each candidate