Co-Op, Enterprise Go-to-Market

About the role

Lila is seeking a fall co-op to support the Enterprise Go-to-Market team as the company builds more proactive, structured, and data-informed ways to prioritize customer opportunities. In this role, you will sit close to enterprise, product marketing, and commercial workflows, helping the team translate customer signals, competitive intelligence, CRM data, and market research into clearer enablement systems. You will own projects that turn complex inputs into practical frameworks that help the enterprise team make faster, more consistent decisions. The position emphasizes hands-on work across functions, giving you direct exposure to how Lila identifies priority customers, supports strategic deals, and builds the operating tools that scale as the commercial organization grows. You will be expected to ask insightful questions, challenge assumptions behind existing go-to-market approaches, and propose improvements based on evidence you gather and synthesize. This is an ideal opportunity for someone who wants to learn how an AI-first science company structures its commercial engine and who is motivated by seeing ideas turn into repeatable processes.

Key facts

What you'll do

Support research on customer segmentation, opportunity prioritization, and strategic account focus areas by reviewing internal and external data sources.

Help organize customer, market, and CRM data into usable GTM enablement resources that sales and product teams can reference and reuse.

Synthesize signals from customer conversations, market research, and internal data into clear briefs and summaries that guide stakeholder decisions.

Build lightweight tools, trackers, and workflows that make GTM execution more proactive, including competitive intelligence resources tailored to the AI-for-science landscape.

Contribute to research-backed sales and product marketing materials for enterprise audiences, ensuring positioning reflects data and customer language.

Help clarify how Lila's platform differs from point-solution AI tools, CRO-style approaches, lab automation vendors, scientific software, and model-only providers through structured comparisons and messaging frameworks.

Identify ways AI tools and agentic workflows can improve team productivity, insight generation, and prioritization by designing and testing simple automations.

Assist in mapping commercial workflows to understand where delays, friction exist, and where process changes could improve conversion and expansion outcomes.

Support the creation of dashboards and simple scorecards that help the enterprise team track progress against key segments and strategic accounts.

Partner with commercial, product marketing, and technical stakeholders to translate ambiguous problems into focused research questions and deliverables.

Maintain a living repository of competitive moves, product updates, and go-to-market experiments to inform ongoing strategy discussions.

Help design and run small validation tests of new positioning, content, or outreach approaches to support continuous learning in the enterprise funnel.

Document playbooks and standard operating procedures so that insights from this co-op's work are preserved and actionable for future hires and teams.

Take ownership of end-to-end workstreams where you define scope, coordinate inputs, and deliver a complete artifact that the team can operationalize.

Use qualitative and quantitative inputs to recommend which customer segments and use cases deserve priority in the next planning cycle.

Requirements

Currently enrolled in an MBA program.

Strong interest in enterprise go-to-market, product marketing, revenue operations, or commercial strategy.

High comfort using AI tools to research, structure information, and improve day-to-day workflows.

Able to synthesize messy inputs into clear briefs, trackers, summaries, or recommendations.

Strong written communication skills, with attention to clarity, structure, and audience.

Comfortable working across commercial, product marketing, and technical stakeholders.

Organized, proactive, and able to manage multiple small projects with clear follow-through.

Willingness to learn deeply about scientific domains, commercial models, and how AI is reshaping discovery and commercialization in life sciences and related fields.

Nice to have

Experience in B2B software, enterprise technology, AI, or model-provider environments.

Familiarity with CRM tools, GTM tooling, market research, or customer segmentation.

Interest in AI for science, scientific software, lab automation, or technical commercialization.

Technical academic background in life sciences, physical sciences, engineering, computer science, or a related field.

Practical notes

This co-op role is based in Cambridge, Massachusetts, and aligns with standard academic co-op schedules. Applicants should be prepared for an interview process that may include assessments relevant to commercial thinking and written communication. Lila Sciences is an equal opportunity employer and welcomes applicants from diverse backgrounds to apply, even if they do not meet every qualification listed above.