Research Scientist

At Orakl Oncology, we are accelerating the development of oncology treatments. Today, fewer than 5% of new cancer drugs succeed in clinical trials. Clearly, new methods are needed. We combine cutting-edge biology and AI to build the next generation of insight platforms with the world’s largest cohort of patient tumor avatars. These avatars fuel our AI-powered predictive engine, helping to anticipate clinical trial outcomes, validate therapies, and uncover new drug candidates. By generating multimodal, real-world data, we deliver insights that consistently outperform existing solutions.

Our mission is simple yet ambitious: to bring more effective treatments to patients who need them and to make drug development smarter, faster, and more personalized. We collaborate with top hospitals, research institutes, and pharmaceutical companies worldwide. Backed by leading investors, we are a fast-growing, mission-driven startup at the intersection of science and technology.

We are looking for an exceptional Research Scientist to invent, prototype, and mature the next generation of models powering target and biomarker discovery from our PDO multi-omics and functional data, one of the rarest datasets in oncology.

This is a research-first, individual-contributor role for an outstanding computational scientist-engineer: someone who designs novel methods at the interface of computational biology and AI/ML, evaluates them with real research rigor, writes excellent Python, and ships complex analyses fast. Rare data, open methodological problems, an engineering stack already in place, a structured database and a data model connected to what happens at the bench. You'll work as a pair with a Research Engineer who hardens your validated methods into production, giving you rare leverage to move from idea to deployed method quickly. You'll report to the Head of Computational Biology.

• Develop new computational methods to analyse and interpret Orakl’s multi-omic data. This will include novel methods for target identification, validation, and stratification to initiate nomination.

• Connect your innovative analysis methods to platform and engineering teams to develop our product portfolio. Work in close collaboration with the research engineers to develop production-grade deliverables for the internal and external stakeholders.

• Collaborate cross-functionally to build compelling data packages that drive program milestones and internal investment decisions.

• Contribute to the team's scientific culture: rigor, reproducibility, data integrity and biological relevance.

Must-have

• PhD plus postdoctoral experience in computational biology including ML or ML applied to genomics — with a track record of independent research (publications, methods you've driven end to end).

• Strong experience in transcriptomic / multi-omics analysis (RNA-seq and ideally beyond: WGS/WES, other omics).

• Proficient in Python and its data/bioinformatics ecosystem (e.g. scanpy, PyDESeq2, scikit-learn, pandas).

• Comfortable with classical ML; exposure to causal inference or network methods is a strong plus.

• Proactive and capable of doing applied research with a high-level of autonomy, with no immediate managerial ambition.

• Delivery-oriented pragmatism: able to scope an MVP, accept an imperfect first pass, and iterate quickly.

• Autonomy and rigor: genuine care for data integrity and reproducibility.

• Fluent in English.

Nice to have

• Knowledge of cancer biology (KRAS biology, DDR/HRR, Wnt pathways).

• Experience with functional drug-response data (pharmacogenomics, screening).

• Familiarity with preclinical models such as organoids / PDOs.

• Solid software practices (Git, reproducible environments) to smooth the handoff to engineering.

• Individual contributor: your time goes to science.

• Research Scientist ↔ Research Engineer pairing: you explore and validate, your counterpart puts it into production. Short loop, direct communication.

• Internal research: freedom to explore, with the expectation of delivering actionable MVPs.

• Iterative and pragmatic: we prefer an imperfect but interpretable first result, shared early, over a "perfect" analysis delivered too late.

• Candid communication: we expect substantive pushback, not validation.

• Mission: work in a cutting-edge environment, on the forefront of cancer research.

• Role: be in a high-impact research role at the core of a distinctive oncology discovery platform, where you will have access to rare PDO multi-omics and functional datasets, and a dedicated engineering counterpart to turn your methods into durable building blocks.

• Impact: participate and have ownership in a fast-growing, multi-faceted project at the early stage of a growing start-up, with the opportunity to shape it; a role where your technical expertise will have a tangible impact on patient outcomes.

• Team: join a collaborative, multidisciplinary team where you’ll work alongside stellar clinicians, biologists, and engineers towards a common mission.

1. HR Call: Getting to know each other, aligning on expectations and context.

2. Technical Deep Dive: A deep conversation on your experience with production data pipelines, data modeling, and scientific data processing.

3. Technical Case: A system-design and problem-solving exercise representative of the real wet-lab data challenges you'll face at Orakl.

4. Reference Call: A conversation with one or two people you've worked with closely.

5. Founder Interview: A final discussion with our founders on vision, culture fit, and mutual ambitions.

Orakl Oncology is committed to diversity and equal opportunity. All qualified applications will be considered.