
Orakl Oncology is a pioneering precision oncology company founded in 2023 as a spin-off from the Gustave Roussy Institute, focused on accelerating oncology drug development through a first-in-class…

Orakl Oncology is a pioneering precision oncology company founded in 2023 as a spin-off from the Gustave Roussy Institute, focused on accelerating oncology drug development through a first-in-class…
Founded: 2023
Headquarters: Villejuif, Île-de-France, France
Focus: AI-powered precision oncology platform for drug development
Flagship products: O-Predict; O-Validate
Notable funding: Seed round (Dec 2024); ~€11M–€15M total raised
28 employees
Cancer drug development, with an emphasis on colorectal and pancreatic cancers
2023
Biotechnology Research
≈€11,000,000
Seed round announced Dec 3, 2024; reporting cites nondilutive support from Bpifrance and total capital raised to date near €15M in some sources.
“Bpifrance; Singular; additional investors recorded (Amazon Web Services, HCVC, SistaFund, Speedinvest, Verve Ventures)”
At Orakl Oncology, our wet lab generates a rich diversity of data — from high-throughput screening results to microscopy images and various forms of unstructured data. Our ambition is to scale this data production and centralize it into a robust, industry-grade data platform. To get there, we need to address several challenges: scalability, data quality, and cross-team accessibility.
We are looking for a Data Scientist Intern to help design, build, and optimize the workflows that power our wet-lab operations. In this role, you’ll work at the intersection of biology and data science, contributing directly to projects with immediate impact on our mission to discover new cancer treatments.
Your next opportunity is in here somewhere. Sign up to explore 70,000+ startups and their open roles. No spam. No gamification. Just jobs.
70,000+
Startups
83,000+
Open Roles
4,500+
New This Week
| Company |
|---|
Key Responsibilities:
Collaborate closely with Orakl’s biologists to build and improve data ingestion workflows, including automated error-detection pipelines and multimodal data integration.
Analyze wet-lab–generated data to guide experimental decisions, applying statistical methods to quantify the impact of experimental conditions on data quality and reproducibility.
Develop and maintain internal tools (dashboards or web applications) that make wet-lab data accessible and actionable for cross-functional teams.### Minimum Qualifications
BSc, MSc, MEng, or equivalent degree in statistics, machine learning, data science, or a related field.
Prior professional experience using Python, with familiarity with modern development tools and environments (GitHub, AWS/GCP).
Strong foundation in applied statistics for experimental data (e.g., variability analysis, hypothesis testing, drift detection).
Hands-on, curious, and autonomous, with the ability to navigate fast-paced and ambiguous environments.
Genuine interest in the intersection of data and biology.
Proficiency in English.