Research Lead, Predictive Oncology

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 relevant oncology and computational conferences, present your team's research, and turn scientific discussions into joint projects and publications.

Run publication governance: coordinate authorship and co-author review with hospital and pharmaceutical partners, track contractual publication and acknowledgement obligations, and align disclosure with confidentiality, patient-data permissions and intellectual-property considerations.

Minimum Qualifications

• PhD, PharmD, MD, or MSc with an equivalent research record in a quantitative or biomedical discipline relevant to predictive oncology.

• Demonstrated ownership of a research programme: you have formulated a scientific agenda, prioritised competing questions and led projects from initial hypothesis to validated findings.

• Experience leading and mentoring scientific contributors, including coordinating work across disciplines and critically reviewing others' methods and conclusions.

• A strong publication record in translational oncology, computational biology, biostatistics and/or ML for health, with clear intellectual and analytical contributions.

• Hands-on proficiency in Python and SQL: you can work with internal data and libraries, develop and evaluate methods, and produce reproducible analyses and figures.

• Strong grounding in statistical inference, predictive modelling and study design; able to challenge biological assumptions and machine-learning evaluations.

• Excellent scientific writing and oral communication in English, including manuscripts, scientific presentations and substantive discussions with external researchers.

Preferred Qualifications

• Experience leading R&D in a biotech, health technology or translational research setting, with responsibility for milestones and resource planning.

• Experience translating research methods into a predictive product or decision-support workflow.

• Familiarity with patient-derived models, drug sensitivity assays, clinical trial design and oncology endpoints.

• Experience with independent or prospective validation, multimodal data integration, survival modelling, or pharmacological modelling.

• Experience establishing academic or clinical collaborations and publishing with pharmaceutical partners.

• Familiarity with appropriate reporting standards and reproducibility practices, including protocol registration and code and data availability statements.

• Experience presenting research at oncology or computational conferences and maintaining an active scientific network.

Who You Are

• A scientific owner: you turn an ambitious objective into a focused research agenda and make explicit choices about what deserves the team's time.

• A hands-on leader: you develop others while staying close enough to the data and code to challenge results and solve difficult problems.

• Rigorous and intellectually honest: you seek evidence that could disprove your hypotheses, distinguish exploratory findings from validated claims, and report limitations clearly.

• Curious across disciplines: you connect experimental biology, clinical questions and quantitative methods.

• An effective collaborator: you build productive relationships, navigate scientific disagreement and turn external discussions into research progress.

Why Join Orakl

• Own a research programme with a clear scientific identity. Shape O-Predict's direction and build a body of work around the translation of experimental treatment responses into clinical predictions.

• Study a distinctive combination of data. Bring together patient-derived tumor models, functional drug screening and molecular profiles, incorporating clinical information where available. Explore questions that require these modalities to be studied together.

• Help decide what gets measured next. Work with experimental and clinical colleagues to design new studies that test your ideas and address the limitations of existing data.

• Work across disciplines every day. Develop methods alongside the scientists generating the measurements, the engineers supporting their use, and collaborators who understand the clinical questions.

• Build your scientific reputation. Lead original research, manuscripts and conference contributions, and develop sustained collaborations around your programme.

• Connect discoveries to consequential decisions. Develop and validate methods intended to inform therapy evaluation and clinical development, with a clear account of what the evidence does and does not support.

• Grow as a research leader. Mentor researchers and help shape the capabilities and future team needed to deliver the programme.

Interview Process

1. HR Call: Align on motivation, role scope and expectations.

2. Scientific Deep Dive: Discuss a research programme you led: the questions you chose, your own technical contributions, validation decisions, team leadership and published outcomes. We will share a confidential scientific briefing on O-Predict's current evidence, available data and open questions so you can assess the opportunity.

3. Research Strategy and Analysis Discussion: Using a bounded, representative evidence package, discuss research priorities and a validation plan, critique an analysis, and outline how you would communicate the findings. Meet the experimental and computational colleagues you would work with and discuss the studies you would want to pursue.

4. Reference Call: Speak with one or two close collaborators who can assess scientific judgement, research ownership and leadership.

5. Founder Interview: Discuss O-Predict's scientific direction, decision-making responsibilities, research resources, publication arrangements and mutual ambitions.