What's the role and how do you fit in?
BenevolentAI, founded in 2013, is an advanced technology company focused on accelerating the journey from data to medicines. It is the world’s only technology company with end-to-end capability from early discovery to late stage clinical development.
As a domain expert in the handling and interrogation of patient-level data (e.g. electronic health records, clinical trial data), you will join a fast-growing team using diverse clinical and biological data to translate analytic findings rapidly to drug discovery programmes. As we ingest increasing volumes of clinical data, you will bring your deep experience of integrating such data into research programmes to join a cross-disciplinary team of experts in machine learning, natural language processing, and drug discovery. You will be instrumental in building our capabilities for understanding patient subgroups and druggable disease mechanisms using clinical data. You will develop and implement workflows for clinical data ingestion, processing and analysis, establishing learning from clinical data as a core component of our AI-driven discovery platform.
What will you be accountable for?
Contribute to design and lead interpretation of results from workflows for ingestion and interrogation of a range of patient-level clinical data, including electronic health records, clinical trials data and survey data.
Contribute to the integration of clinical data into drug target discovery and validation, and patient sub-phenotyping projects within Benevolent for phenotype-led target discovery and patient selection for clinical trials.
Lead analyses of patient-level data, using machine learning techniques, for identification and characterisation of patient subgroups defined by clinical features and/or disease trajectories.
Guide the identification and acquisition of new sources of clinical data.
Contribute to existing external collaborations and build new relationships.
Bring expertise in state-of-the-art clinical data ontologies and data formats, and their practical application.
Apply your knowledge of information governance to ensure compliance in ingestion and analysis workflows.\
About the team
The Translational Medicine Squad (TMS) works to bring rich patient-level data into the drug discovery programmes within BenevolentAI, integrating them with the AI and developing new analytic methods for network and causal inference. The TMS team incorporates gurus in machine learning, bioinformatics experts, drug discovery leaders, translational medicine specialists and chemoinformatics working together. We have established a number of collaborations with UK and international academic groups and are building several others.
TMS sets its own objectives and goals that following broader objectives of the company, and works closely with other teams and drug discovery programmes at BenevolentAI.
We’ve assembled an exceptionally diverse, talented and high-energy team who sincerely enjoy coming to work every single day to bring their ideas and real passion for new technology and medicine discovery. You will work alongside recognised leaders with vision at the cross section of Machine Learning and Chemistry data, with plenty of interaction with drug discovery researchers.
What skills, experience, and qualifications do you need?
PhD in health informatics, epidemiology, applied statistics or a related discipline
Deep experience in handling and analysing clinical data from at least two of electronic health records, clinical trial patient data, epidemiological longitudinal observational data.
Experience in handling and analysing longitudinal discrete and continuous data at the individual patient level.
Exceptional skills in the analysis of longitudinal, high dimensional clinical data.
Effective communicator of methods and findings to a broad audience including non-experts
You are comfortable working in a fast-paced environment and cross-functional teams
Excellent communicator, both verbal and written, with ability to influence at all levels and in across departments.
Nice to haves
Experience in handling and analysing free text health records, including a working knowledge of natural language processing.
Excellent knowledge of, and practical competence with clinical data ontologies, data models and formats.
Working knowledge of drug discovery and development
BenevolentAI, founded in 2013, is an advanced technology company focused on accelerating the journey from data to medicines. It is the world’s only technology company with end-to-end capability from early discovery to late stage clinical development. The company is HQ’d in London with a research facility in Cambridge (UK) and further offices in New York and Belgium.
The ‘Benevolent Platform’ is a unique machine intelligence technology system built to mine new knowledge from vast quantities of biomedical data, propose treatments and design drugs to enable its world leading scientists to bring new treatments to patients faster.
We are working on applying tech to real problems, and see real outcomes and the fruits of our labour by working on a meaningful mission. We do our job ‘Because it matters’ and live by the philosophy that unconventional thinking together with purposeful technology can have an impact on humanity.
The working environment is agile and we work in cross functional teams. We encourage a culture of learning, developing and challenging the status quo to foster dynamic, entrepreneurial behaviours, innovation and a ‘fail fast’ mentality.
Alongside all of this we can offer excellent benefits (learn more at https://benevolent.ai/careers/), a global reach and the ability to work with the best talent in the industry.
Please be advised that we will hold and process your Personal Data for continuous recruitment purposes (this is irrespective of your success in this application or not) in line with vital regulatory requirements. Under GDPR/DPA2018, you have the right to be informed, access, restrict, correct or ask us to delete your Personal Data. More details available on our Privacy Notice (https://benevolent.ai/privacy/).