
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 AI-powered techbio platform. The platform integrates real-world patient data with advanced biology to create individualized patient tumor avatars that simulate drug responses, improving clinical trial success rates and patient recruitment. Orakl markets two AI-powered commercial products, O-Predict and O-Validate, which forecast patient responses and provide biological evidence for target validation, respectively. The company targets colorectal and pancreatic cancers, addressing major unmet medical needs, and aims to transform drug development by enabling better decision-making and strategic partnerships with pharmaceutical companies. Orakl has raised nearly €15 million in funding and is building a dedicated business team to support its growth and market entry.

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 AI-powered techbio platform. The platform integrates real-world patient data with advanced biology to create individualized patient tumor avatars that simulate drug responses, improving clinical trial success rates and patient recruitment. Orakl markets two AI-powered commercial products, O-Predict and O-Validate, which forecast patient responses and provide biological evidence for target validation, respectively. The company targets colorectal and pancreatic cancers, addressing major unmet medical needs, and aims to transform drug development by enabling better decision-making and strategic partnerships with pharmaceutical companies. Orakl has raised nearly €15 million in funding and is building a dedicated business team to support its growth and market entry.
Founded: 2023 (spin‑out from Gustave Roussy)
Headquarters: Villejuif / Paris region, France
Focus: AI-powered precision oncology using patient-derived tumor avatars (organoids) and multimodal data
Products: O-Predict and O-Validate (AI commercial products)
Known funding: ≈ €14–15M across 2023–2024 (seed / pre-seed rounds)
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Preclinical and clinical de-risking for oncology drug discovery and development, with initial tumor-type focus on colorectal and pancreatic cancers.
2023
Biotechnology Research
€3,000,000
Reported pre-seed/seed round
€11,000,000
Seed round with participation from multiple investors
“Singular; Bpifrance; Speedinvest; Verve Ventures; HCVC; SistaFund; Amazon Web Services”
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.
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.