Missing: Founding Data Scientist (Clinical & Health Data AI) — part builder, part scientist. You love biomarkers, messy CSVs, and clean protocols. You get your hands dirty with code, think statistically, and care about making AI useful in preventive care. You can ship (API + Docker + cloud) and you respect validation. Bonus points if you’ve played with UK Biobank or similar. If you’re an MD or Clinical Biologist who can do all that, you’re our unicorn (please DM me).
When found, please return as a whole to Max.
When interested, check out this video: https://www.youtube.com/watch?v=Q1xRESEXwWk
Please check minimum requirements below before applying.
Join Eliksir as the second co-founder to take the product from Zero(.5) → One and beyond. We’re building an AI Lab Assistant for clinical biology labs — rule-based + LLM reasoning today; a defensible, data-driven engine tomorrow. Short term, you’ll shape biomarker logic, stand up the MVP service (API/Docker/cloud), and co-author a retrospective proof-of-value protocol. Mid-term, you’ll co-lead clinical-grade validation and develop proprietary models on larger datasets. B2B→D (labs → doctors) model.
You’ll work closely with Max (pharmacist, longevity nerd, real connector and creazy idea generator).
Must-haves:
* Hands-on builder: strong DS/ML fundamentals; ships code (Python preferred; other DS stacks OK).
* Build a model service end-to-end: FastAPI/Flask, Docker, REST/JSON.
* Deploy & operate on cloud (Azure ideal; AWS/GCP fine), with basic logging/monitoring and CI/CD.
* Clinical tabular pipelines: ETL with Pandas/SQL, data QA, missing-data strategies, feature engineering; reproducible datasets.
* LLM + rules: implement hybrid LLM/RAG + rule logic with guardrails; write simple evaluation tests for prompts & outputs.
* Classical ML: trees/boosting, clustering, time-series basics; baseline fast, iterate as data grows.
* Clinical validation basics: co-author a retrospective proof-of-value protocol (objectives, inclusion/exclusion, endpoints, stats plan). Evaluate with AUROC/AUPRC + calibration and clear thresholds.
* Governance mindset: works safely under EU health-data constraints (GDPR, de-identification, auditability).
* Founder fit: entrepreneurial, clear communicator with clinicians; comfortable with equity-only and fast learning loops.
Unicorn alert: if you’re an MD (physician) or Clinical Biologist and meet the deploy/ML criteria above, you’re the rare profile we dream about --> DM directly.
Important: unpaid and part-time until pree-seed funding. After initial raise, join full-time with a balanced founder package.
Practicalities: EU-based (or EU-friendly time zones). Equity co-founder with a 3-month part-time trial.
Bonus points:
* Interoperability & production: FHIR/HL7, LOINC; prior hospital/LIMS/EHR deployments.
* Large clinical datasets & longitudinal modeling: UK Biobank, OpenSAFELY, MIMIC, eICU, NHANES; survival/time-to-event methods.
* MLOps depth: MLflow/W&B, DVC/LakeFS, drift monitoring/alerts.
* LLM orchestration: RAG pipelines, vector stores (pgvector/Pinecone/Weaviate); on-prem/VPC LLMs.
* Azure experience: Azure ML, Container Apps/App Service, Key Vault, Monitor; IaC (Terraform/Bicep).
* Grants/publications; FR/NL/EN working proficiency; startup/consulting track record.