Research Lead, Predictive Oncology

Job Description

About Orakl Oncology At Orakl Oncology, we combine patient-derived tumor models, experimental drug screening and computational modelling to improve oncology drug development. Our research brings together functional treatment-response measurements, molecular profiles and clinical information where available, creating opportunities to investigate how experimental responses translate into patient outcomes. Our platforms are designed to help anticipate clinical trial outcomes, evaluate therapies and identify new therapeutic opportunities. 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 personalised. 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. About the Role We are looking for a Research Lead - Predictive Oncology, to build and lead a research programme to improve the translational capabilities of Orakl Oncology’s platform, and to provide the scientific backbone of O-Predict, Orakl’s predictive solutions. You will own O-Predict's long-term scientific roadmap, choose the questions we pursue, and lead the work needed to answer them. The opportunity combines access to Orakl's experimental and molecular data, clinical information where available, and the ability to shape future data generation with our experimental and clinical collaborators. This is a hands-on scientific leadership role . You will develop and test methods, mentor contributors, and build a coherent body of research with a recognisable scientific contribution. Publications, presentations and external collaborations will be integral outputs of that programme. You will work closely with the CTO and our Data Science, Computational Biology, experimental, product and engineering colleagues. Your research will establish which predictions are reliable, identify their limitations, and inform how O-Predict can support drug-development decisions. The role is suited to a researcher ready to take ownership of an independent programme within a collaborative company, including an emerging research leader with a strong record of original work. What You'll Do Own O-Predict's long-term research roadmap: define and maintain a 2-year scientific agenda, translated into a prioritised 12-month execution plan. Identify the key uncertainties, research hypotheses, data requirements, dependencies and resources; set milestones and explicit criteria for continuing, redirecting or stopping projects. Review priorities with the CTO as evidence and company needs evolve. Lead R&D execution: take research programmes from question formulation through method development, validation and interpretation. Coordinate agreed contributions across Data Science, Computational Biology and experimental teams, establish project ownership, run scientific reviews, resolve blockers and mentor researchers. Remain hands-on in the analyses and modelling decisions that determine scientific quality. Shape future data generation: identify the experiments, measurements and cohorts that would resolve important scientific uncertainties. Co-design these studies with experimental and clinical colleagues, prioritise them within the agreed research allocation, and use the results to test and revise your hypotheses. Advance O-Predict's predictive capabilities: investigate how drug screening, patient-derived models, molecular and clinical data can improve treatment-response and trial-outcome predictions. Assess extensions to new cohorts, indications and therapeutic settings, specifying the evidence needed before extending claims. Own the validation strategy: define relevant endpoints, external baselines and evaluation protocols. Lead independent and prospective validation plans with clinical and experimental collaborators, and document both performance and failure modes. Connect research to product development: work with product and engineering colleagues to translate promising findings into reproducible methods and clearly documented scientific requirements. Define the evidence needed for transfer into O-Predict and assess feasibility, expected value and limitations. Lead publications and scientific materials: build a publication and conference plan around the research roadmap. Lead manuscripts from analysis and figures through submission and reviewer responses, with contributions distributed across the research team. Produce abstracts, posters, talks and methods notes; provide scientific direction and review for partner evidence decks. Build substantive scientific collaborations: work directly with clinical investigators and quantitative researchers to formulate questions, co-design studies, access complementary expertise and pursue independent validation opportunities. Represent O-Predict at relev