Research Analyst

Description

We're hiring a Research Analyst to go deep into that world: understanding how professors, researchers, and inventors actually work, what tools and workflows they rely on, and where they get stuck turning ideas or data into something others can see, explore, and build on. You'll build a real, structured picture of how research communication happens across academia and R&D today — and build real relationships with the people doing that work.

This is a research and relationship-building role, not a sales role. There's no quota, no script, no pressure to "close" anything. It's designed for someone who wants serious, hands-on experience engaging with the global research and scientific community — reaching out directly to accomplished, highly qualified people the same way you'd reach out to a potential PhD advisor, collaborator, or co-author. You'll be working alongside an AI engineering and product team building genuinely advanced technology, with a front-row seat to how enterprise-grade AI products actually get designed, tested, and shaped by real user needs — experience you'll be able to draw on and apply in your own future research, product, or academic career.

What You'll Actually Do

Map the landscape. Identify professors, researchers, and labs working on interesting or novel problems across your assigned fields/regions — the same kind of scouting you'd do when researching PhD programs or advisors.

Generate discovery conversations. Reach out directly to researchers via email, LinkedIn, or academic networks to understand their workflows, pain points, and how they currently present or share their work and data.

Run structured interviews / informal calls. Ask good questions, listen well, and document what you learn — building an internal knowledge base on how research communication actually happens in the wild.

Represent InNeed honestly. When relevant, introduce researchers to the AI products we're building and gather their feedback — you're not selling, you're validating and learning, and channeling genuine interest back to the product team.

Synthesize findings. Turn conversations and data into structured reports, personas, and insights the product and founding team can act on.

Who You Are

Currently pursuing, or aspiring toward, a PhD or advanced research career — you understand how academics think, communicate, and evaluate new tools, because you're on that path yourself.

Comfortable writing to and speaking with senior, highly qualified people (professors, PI's, industry researchers) — confident but respectful outreach.

Strong written English; able to write a compelling, non-generic cold email.

Organized — comfortable tracking dozens of conversations and turning them into clean documentation.

Genuinely curious about research workflows, data visualization, and how AI is changing how science gets communicated.

What You'll Gain

Direct, high-volume practice communicating with professors and researchers globally — a skill directly transferable to your own PhD/grad school outreach.

A real body of work: interview notes, market maps, and research reports you can point to.

Close exposure to how enterprise-grade AI products — generative AI, RAG, applied ML — are actually designed and shaped around real user needs, from a team already delivering this technology to universities and research institutions.

A working understanding of advanced AI tooling and workflows you can carry into and integrate with your own future research, degree, or career.

Flexible, hybrid or remote work.

What Success Looks Like

No fixed sales targets. Instead, we'll look at things like:

Number of substantive research conversations conducted per week/month

Quality and depth of documented insights

Growth of our internal map of the research community in your focus area(s)

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