Enterprise Account Lead, Chemicals
$88,000–$144,000 year
On-siteCambridge, Massachusetts, United States
Job Summary
Build and grow a high-quality pipeline against clear revenue targets, managing opportunity identification through proposal, negotiation, and close. Lead complex, multi-stakeholder sales cycles across R&D, platform/technology, and business leadership while maintaining accurate revenue forecasts and deal records. Own a portfolio of strategic accounts end-to-end, including account research, meeting prep, pitch deck creation, and stakeholder management. Partner with product and science teams to scope engagements, manage technical RFI responses, and refine the revenue CRM and GTM playbook. This role blends technical depth with business acumen to translate closed-loop AI and autonomous lab capabilities into signed enterprise contracts across catalyst, flow chemistry, and advanced materials.
Required Qualifications
- BS, MS, PhD, or equivalent experience in chemistry, physics, chemical engineering, or adjacent fields
- 2-5+ years in a client-facing role where you owned outcomes for customers, across BD, enterprise sales, management consulting, customer success, or applied science (industrial, energy, or materials)
- Comfortable in a fast-moving, highly technical, cross-functional environment where the sales process, materials, and role itself are still being shaped
- Strong written and verbal communication, able to translate technical depth into clear, customer-ready narratives for both R&D buyers and procurement teams
- Highly organized and execution-oriented, comfortable managing multiple live accounts in parallel with limited oversight
- Collaborative, low-ego working style, able to build trust quickly with PMs, scientists, engineers, and product leaders
- Hands-on use of AI tools in day-to-day workflows and curiosity about how AI and autonomous science will reshape physical science
Desired Qualifications
- Working familiarity with AI/ML applied to physical sciences: property prediction, generative models for materials, Bayesian optimization, simulation-driven design
- Direct exposure to enterprise R&D buying cycles in materials, semiconductors, or industrial biotech
- Prior experience in frontier AI (SaaS, biotech, robotics, or data) selling into technical buyers
- Prior experience as a founder or early GTM/commercial hire helping define early processes, playbooks, and collateral
- A point of view on where AI is and is not useful in physical sciences R&D today
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