We are looking for a talented Senior Bioinformatics Data Scientist to join the Product & Tech division.
You will work in a cross-functional team applying cutting edge approaches to multimodal data integration for drug discovery applications.
Together with colleagues in the Informatics function, you will define and implement processes in line with industry best practice to orchestrate data transformations at scale across a wide range of data sources from chemical, biological and clinical domains.
Responsibilities Provide domain expertise and disseminate best practices in the processing and application of biomedical data within a multidisciplinary team of data scientists, machine learning specialists and software engineers.
Work together with software engineers to productionise informatics logic and algorithms to power user-facing tools that are applied in BAI drug discovery programmes.
Implement and apply quality control checks to maintain scientific and technical data integrity.
Contribute towards the definition of consistent best practices for data integration pipelines.
Collaborate and communicate effectively across product, technology, and drug discovery scientific disciplines and functions to achieve BenevolentAI strategic goals.
We are looking for someone with A PhD, or equivalent industrial experience in bioinformatics, statistics or other computational subjects with application to biology.
Proficient programmer in at least one language and able to demonstrate the potential to comfortably work in Python within a few months.
Familiarity with data pipeline systems (e.g.
Airflow), database query languages (e.g.
SQL) and/or Apache Spark.
Experience working with and integrating a wide range of biomedical data types and resources that include databases such as Gene Ontology, Reactome, Chembl; technologies such as text mining/NLP; and analysis of primary datasets such as genomics/genetics, protein structure, chemistry, or clinical data.
Experience, preferably in industry, in developing and applying computational methods to derive novel insights from data to support one or more parts of the drug discovery process (e.g.
target identification, molecular design, patient stratification).
Excellent communicator, both verbal and written, with an ability to share knowledge and ideas between scientific and engineering disciplines.