relationrx - Senior Data Scientist - Single Cell & Spatial
Requirements
• Familiarity with single-cell transcriptomics or patient-derived datasets. • Knowledge of ML techniques applied to biological data. • A background in statistical modelling and algorithm development. • Experience working in interdisciplinary teams. • 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
• Develop and implement computational workflows for integrating and analysing multi-omics data. • Design statistical models for analysing transcriptomics and other omics datasets. • Use domain and data insights to design meaningful and challenging evaluation tasks for ML models. • Collaborate closely with ML modellers to develop model architectures. • Work with experimental teams to design and validate computational hypotheses. • Present findings and methodologies to internal stakeholders and contribute to publications. • A PhD in computational biology, bioinformatics, or a related quantitative field. • Extensive experience in multi-omics data analysis, including transcriptomics. • Proficiency in Python and familiarity with high-performance computing environments.
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