Director, Data Analytics, And Digital Biomarkers

The Director Digital Biomarkers and Analytics is responsible for developing "fit for purpose" Digital Endpoint strategy to enable GSK's portfolio with Data driven decision making. Developing Machine learning models to identify the right digital measures/endpoints that correlate to severity of the disease by analyzing digital Biomarker data from sensors, wearables, and relevant historical health data. The role is also responsible for working closely with Head of Digital Biomarkers in implementing the digital biomarker roadmap for priority disease areas.

The role addresses the following strategic and technical objectives:

Strategic: Strategic partnership with internal stakeholders, external experts and organizations to advance the acceptance of Digital Biomarkers. Work with Senior Leaders from multiple departments (Digital Health, Regulatory Affairs, Biostat, Clinical Sciences, Clinical Operations, R&D Tech, and other relevant functions) to propose, influence, select and implement the right digital measures that are aligned to the organizational priorities and the Integrated Evidence Plans. Technical: Experience developing machine learning, or deep learning models, signal processing algorithms, and modelling specific human functions, behavioural or physiological characteristics from digital measures or digital health data to get insights about the disease. Prepare proof-of-concept, demo/prototypes, design and execute pilot studies with mobile sensors. Lead team with proactive, hands-on leadership and real-time feedback and coaching. Develop talent and grow the capabilities of the team Lead the Concept Elicitation, User Validation, Analytical Validation, and Clinical Validation of fit for purpose digital measures/endpoints. Key Responsibilities [Delivery]: Responsible for supporting multiple concurrent digital clinical studies (global presence) across GSK Disease Areas. [Team leadership]: Work in a matrix environment and co-lead a sub-team of digital data scientists ensuring the most innovative AI/ML, robust mathematical approaches are utilized to analyze and interpret the digital data. [Regulatory]: Contribute to GSK regulatory strategy to ensure the technology and digital measures developed can enable valid 'primary/secondary endpoints' in therapeutic clinical studies. Be a leader in embedding regulatory grade digital endpoints into development programs. [Innovation]: Identify and guide external initiatives (academic and public-private-partnerships) to accelerate development and uptake of digital solutions by the wider healthcare community. Basic Qualifications: PhD. in Computer Science, Biomedical Engineering, Electrical Engineering, Mathematics, health or biological sciences. Or, alternatively, M.D. with equivalent experience, or combination of education, training, and experience. 10+ years of experience in designing, conducting, and leading biomarker research leveraging wearable devices, health /medical devices, and EHR/EMR health data, and conducting clinical studies. Hands on programming languages such as Python and R, and data analysis tools such as Tableau. Preferred Qualifications: Strong problem-solving and analytical skills, with the ability to identify and mitigate risks associated with AI and ML solutions for Digital Endpoints. Ability to create visualizations from sample wearable/device data to show the art of the possibility. Excellent senior stakeholder management. Excellent communication skills, with the ability to communicate technical information to non-technical stakeholders. Demonstrable knowledge and experience in Image Processing, Machine Learning, Signal Processing, and their applications on mobile sensor data collected from wearables. Strong interpersonal and collaboration skills, ability to present complex information understandably and compellingly, and comfortable working with multi-disciplinary teams. Good understanding of human physiology, and non-invasive sensors used to measure digital biomarkers for health and behavior monitoring. Understanding of human factors, user research, usability, and ensuring that biomarkers provide clear and unique insights and value.
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