relationrx - Data Scientist – Computational Genomics, 12-month FTC
Requirements
• Familiarity with single-cell transcriptomics or patient-derived datasets. • Experience working in interdisciplinary/matrixed teams within biotech or pharma settings. • Understanding of the end-to-end drug discovery process and how genetic evidence informs decision-making. • Personally, you: • Are comfortable working in a matrixed environment, balancing multiple stakeholders and contributing effectively across teams. • Take ownership of your work, proactively seek opportunities to contribute, and enable others to do their best work. • Communicate openly and directly, give and receive feedback constructively, and handle challenging conversations with respect. • Actively seek out diverse perspectives, build strong working relationships, and contribute to shared goals across teams. • Embrace challenges with openness and resilience, set high standards for yourself, and strive to deliver meaningful outcomes. • Working Style & Culture at Relation • At Relation, we operate in a matrixed, interdisciplinary environment, where impact is driven through collaboration across scientific, technical, and operational domains. We collaborate, and you will partner with colleagues across multiple teams and projects, contributing your expertise while aligning to shared company priorities. We work together and win together! • The patient is waiting! • RECRUITMENT AGENCIES: Please note that Relation does not accept unsolicited resumes from agencies. Resumes should not be forwarded to our job aliases or employees. Relation will not be liable for any fees associated with unsolicited CVs. • Relation is a committed equal opportunities employer.
Responsibilities
• Apply, build, refine and integrate statistical models to gain insight from genomics, transcriptomics and other OMICs datasets and support target discovery and validation. • Work cross-functionally at the ML-genetics interface to identify opportunities, solve problems and implement solutions for shared insight • Integrate human genetics evidence with OMICs datasets (e.g. transcriptomics, proteomics) to uncover disease mechanisms and prioritise actionable targets. • Develop scalable computational workflows for reproducible analysis within Relation’s existing stack • Partner closely with experimental and machine learning researchers to validate hypotheses, interpret results, and guide downstream studies. • Communicate findings clearly to internal stakeholders, including presenting methods, results, and recommendations. • Contribute to publications, scientific communications, and project documentation, supporting scientific excellence and external visibility. • PhD in statistical genetics, genomics, computational biology, machine learning, bioinformatics, or a related quantitative field. • Knowledge of machine learning techniques applied to biological data • Experience in quantitative genomics, statistics, bioinformatics, or multi-omics data analysis. • Proficiency in Python (preferred), or R, and familiarity with high-performance computing environments, collaborative coding and version control (e.g. git)
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