About the Role
Are you interested in helping to shape the era of AI? From healthcare to manufacturing to education and constituent services, AI is transforming entire industries and fundamentally changing the way we live and work. Microsoft is helping customers directly embrace this opportunity to transform their businesses. Trusted by organizations around the world with mission-critical application workloads, Azure is the place where organizations can build with AI securely, responsibly, and with confidence. The Azure AI team is a growing organization that consists of Specialist and Technology experts, who focus on specific domains, industries, and workloads, such as Azure OpenAI Service, Azure AI Services, Application Development and Machine Learning.
As the Global AI Go-To-Market Strategy Lead, Machine Learning, you will work within our worldwide Data & AI commercial solution area, collaborating with internal organizations, to build and execute the global sales strategy for the Machine Learning portfolio on Azure, with a focus on Azure Machine Learning and AI Infrastructure. You will create and lead sales programs that solve customer challenges leveraging machine learning patterns. You will partner with Marketing and Engineering teams to share customer feedback and influence machine learning product strategy. You will be accountable for overall business revenue for machine learning and AI infrastructure, owning the rhythms to review key reporting metrics and addressing market opportunities.
Responsibilities
Go-To-Market (GTM) Leadership and Program Development
- Lead the design, development, and implementation of AI-led sales programs and plays, especially in collaboration with key Microsoft partners, to grow the overall market share of Azure Machine Learning and relevant ML products, especially for custom and opensource Large Language Models (LLMs).
- Coach worldwide sales teams, including individual contributors and sales managers, for effective execution of machine learning and AI infrastructure programs.
- Collaborate with AI sales leads, the partner organization, marketing teams, engineering, and other teams to drive effective coordinate cross-business strategies.
Ownership of worldwide business results and insights
- Accountability for the growth and target attainment of the worldwide AI consumption budgets, specifically for Machine Learning and AI Infrastructure.
- Actively drive and manage the performance of the AI sales programs, demonstrating positive sales programs impact on Machine Learning and AI Infrastructure consumption and adoption, particularly for pipeline generation and acceleration.
- Identify, capture, and communicate customer, partner, and seller feedback and insights on sales program effectiveness and use this to inform the sales strategy and program evolution, as well as surface through key business review rhythms.
Product and Technology Leadership
- External and Internal thought leadership engagement to raise awareness land key messaging of Machine Learning portfolio and how the technology delivers business outcomes.
- Learnings and insights on the strengths and weaknesses of competitive offerings and market trends related to Machine Learning.
Qualifications
Required/Minimum Qualifications:
- Bachelor's Degree in Business Administration, Marketing, Finance, Engineering, or related field AND 5+ years experience in business consulting, sales, sales operations, information technology (IT), account management, business development, marketing, IT field sales, or a related field OR equivalent experience.
- 6+ years experience in in Near Term Strategy (2 years out), Management Consulting, Sales, or Finance.
Additional Or Preferred Qualifications:
- 3+ years of AI (Machine Learning, Deep Learning) experience in enterprise public cloud-based solutions.
- 5+ years of experience either directly selling, or building sales programs, associated with Machine Learning and AI Infrastructure.
- Advanced understanding of data science processes and machine learning lifecycles within an enterprise organization (ML Ops, model management, data integration, responsible AI, etc.).
- Experience with AI-specialized infrastructure (e.g. NC/ND Series VMs).
- Common understanding of industry or functional scenarios that include machine learning (ex: predictive maintenance, recommendation engines, demand forecasting, knowledge mining, etc.).