Data Scientist | Cambridge | Biotech (Drug Discovery)

We are driven by the mission to develop novel, targeted therapies for cancers with significant unmet needs, using cutting-edge computational methods and next-generation cancer models. Join us and be part of a team that is revolutionizing drug discovery.
Key Responsibilities:
Collaborate with cross-functional teams including biologists, chemists, and computational scientists to drive oncology drug discovery through data-driven insights.
Apply advanced statistical, machine learning, and computational techniques to analyze large-scale multi-omics, genomic, and clinical datasets, accelerating the identification of novel cancer targets and biomarkers.
Develop and optimize predictive models to identify therapeutic response patterns and enhance patient stratification for cancer clinical trials.
Build and implement scalable data pipelines and workflows for high-throughput drug screening and mechanistic studies.
Integrate internal and external datasets to generate actionable insights into cancer biology, drug mechanisms, and disease progression.
Present findings and data-driven insights to stakeholders, influencing drug development strategies.
Stay at the forefront of advancements in data science, machine learning, and computational biology to continuously bring innovation to the team.Key Qualifications:
PhD, MSc, or equivalent experience in data science, bioinformatics, computational biology, or a related field.
Proven experience applying data science and machine learning to biological or clinical datasets, ideally within oncology or drug discovery.
Proficiency in programming languages such as Python, R, and experience with data analysis libraries (e.g., batch, TensorFlow).
Strong understanding of statistical modeling, machine learning algorithms, and multi-omics data analysis (e.g., genomics, transcriptomics, proteomics).
Experience working with large-scale biological databases and integrating multi-modal datasets.
Excellent problem-solving skills and ability to work both independently and in a team-oriented environment.
Strong communication skills, with the ability to present complex data findings to both scientific and non-scientific audiences