Data Scientist Cambridge Biotech (Drug Discovery)

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Job description

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.

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