Overview
Skills
Job Details
AI & Analytics Pre-Sales Lead | Location: Remote | Full Time
Job Description
An AI & Analytics focused Pre-Sales leader to own activities like proposal creation, pitching AI & Analytics offerings to customers and collaborating with Marketing to launch AI & Analytics focused campaigns
Roles & Responsibilities
Modern data platforms, machine learning (ML) and AI are major focus areas for our AI and Analytics Practice, and we re looking for a Practice Leader to lead the growth. Do you have both technical and business skills? This is a chance to set strategy, define product and service offerings, work with our sales team to win deals, and work with our engineers to build reusable assets and custom solutions.
Responsibilities include:
- Leading the Practice strategy by defining the IP and custom service offerings, target markets, and lead generation approach
- Contributing to Practice sales targets (supporting the efforts of the salespeople, who are primary owners of the quota)
- Influencing industry teams to pitch AI & Analytics offerings to all applicable existing customers and prospects by educating the sales team and helping them identify the best targets within their territories
- Providing sales support in the form of collateral and RFP responses and pitching our expertise at sales meetings and conferences
Requirements:
- Bachelor s degree in computer science, or demonstration of equivalent conceptual knowledge through self-study and prior experience
- 3+ years of experience in a business development, practice leadership, or pre-sales focused on analytics solutions
- 5+ years designing and developing data & analytics platforms at significant scale
- Excellent communication (written and oral), customer-facing presentation and interpersonal skills
- Some understanding of modern AI techniques, sufficient to know what s possible and practical and have conversations with clients on how to use it to drive business impact
- Understanding of cloud and distributed systems principles, including load balancing, networks, scaling, and in-memory vs disk operations
- Experience with cloud platforms such as AWS, Azure, and Google Cloud Platform; container and orchestration technologies