Build with us.
Lippershey is an early-stage pharma intelligence venture with a clear conviction: the intelligence that shapes medicine should be transparent, source-linked, and accessible to teams of every size. We are building focused intelligence products on public data — and we are looking for people who want to help shape what comes next.
These are founding-stage opportunities: high-autonomy, hands-on, hybrid-friendly roles based around Utrecht, Netherlands. Some may be internship, project-based, equity-oriented, advisory, or future full-time roles. If the mission resonates, we would love to hear from you.
Why join now
Help shape products, workflows, data pipelines, and user experiences while the foundations are still being built. This is hands-on work for people who like ownership, ambiguity, and visible progress.
Lippershey focuses on public-data questions across clinical development, patents, market access, innovation landscapes, portfolio strategy, and evidence transparency.
We believe intelligence products should show their sources, methods, assumptions, and uncertainty. The goal is not to hide complexity — it is to make complex public evidence easier to inspect and use responsibly.
Roles are Netherlands-based, with a preference for Utrecht-area hybrid collaboration. Remote-friendly work is possible, but we value focused working sessions, direct communication, and a practical build mentality.
Open roles
We are currently shaping a small founding network of builders, interns, advisors, and collaborators. The roles below can be adapted depending on experience, availability, and fit.
Lippershey is looking for a bioinformatics, data science, biomedical sciences, pharmaceutical sciences, or computational biology student to help build the data foundations behind public-data pharma intelligence products.
You will work directly with the founder on ingestion, cleaning, harmonization, and documentation pipelines for public pharma datasets such as ClinicalTrials.gov, FDA sources, EMA public documents, national or regional patent registers, publication metadata, and other open evidence sources. The work is practical, hands-on, and product-facing: the goal is not to create a classroom exercise, but to help build reusable infrastructure for live intelligence products.
This role is well suited for an MSc student, final-year BSc student, or early-career builder who wants portfolio-grade experience at the intersection of pharma, data engineering, public evidence, and applied AI. It may also be suitable for a thesis, internship, or project-based collaboration depending on university requirements.
- Support ingestion and normalization of public pharma datasets
- Help structure data from clinical trial registries, regulatory sources, publications, and patent-related sources
- Build or improve repeatable data-cleaning workflows
- Document source fields, assumptions, and known limitations
- Support quality checks and source traceability
- Help prepare datasets for dashboards, evidence views, and intelligence briefs
- Work with the founder to translate messy public data into usable product inputs
- Student or early-career profile in bioinformatics, data science, pharmaceutical science, biomedical science, computational biology, health data, or a related field
- Comfortable working with structured data
- Some experience with Python, SQL, spreadsheets, APIs, or data cleaning
- Interest in pharma, clinical development, regulatory data, patents, or open science
- Careful mindset around source quality, uncertainty, and documentation
- Hands-on attitude and willingness to learn quickly
- Experience with Python data libraries
- Experience with Supabase, PostgreSQL, APIs, or scraping public data responsibly
- Familiarity with ClinicalTrials.gov, PubMed, EMA, FDA, patents, or HTA data
- Interest in LLMs, NLP, or AI-supported research workflows
- Experience with GitHub or basic software collaboration
How we work
We value people who can move from idea to prototype, from source to dataset, and from question to usable workflow.
Every intelligence product should make its sources, methods, and uncertainty inspectable.
We prefer Netherlands-based collaborators and Utrecht-area hybrid working, while keeping remote-friendly flexibility for focused work.
This is early-stage work. People who join should expect ownership, ambiguity, fast learning, and direct collaboration with the founder.
Not sure where you fit?
We value curiosity and conviction over perfect credentials. If you care about making pharma intelligence more transparent, source-linked, and accessible, reach out — let's find the right way to build together.