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Data Scientist | Cambridge | Biotech (Drug Discovery), Cambridge
Client:
SoCode
Location:
Cambridge, United Kingdom
Job Category:
Other
EU work permit required:
Yes
Job Reference:
692b9e57766a
Job Views:
6
Posted:
26.04.2025
Expiry Date:
10.06.2025
Job Description:
The candidate should meet the following requirements:
Role 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., TensorFlow, scikit-learn).
* 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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