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Inkfish Principal Statistician and Methodological Lead in Digital Trials

King's College London - Department of AI in Preventive Medicine

Location: London
Salary: Competitive
Hours: Full Time
Contract Type: Fixed-Term/Contract
Placed On: 27th November 2025
Closes: 18th January 2026
Job Ref: 132006

About the role

The post of Inkfish Principal Statistician and Methodological Lead in Digital Trials on the EMBRACE study represents an exceptional opportunity for an internationally recognised expert in statistics with a strong track record in methodological leadership in digital trials to contribute to a globally pioneering programme focused on maternal and early childhood health. The post holder will serve as the Principal Statistician and Methodological Lead in Digital Trials for the EMBRACE programme, providing strategic direction and scientific leadership across the design, analysis, and interpretation of both observational and interventional studies, with a particular emphasis on digital and AI-enabled trials. The post demands strong expertise in implementing methodological frameworks and in clinical epidemiology. A central responsibility will be to lead protocol design, develop methodological frameworks, and oversee statistical analyses, in alignment with regulatory, ethical, and governance frameworks relevant to digital health research and AI-enabled studies (e.g. GDPR, GCP, and AI ethics). The post holder will direct core methodological workstreams within the EMBRACE consortium, and collaborate closely with global partners in academia, healthcare, and industry to drive innovation and maximise translational impact. 

The post holder is required to hold a PhD degree in statistics, biostatistics, epidemiology, health data science, or other closely related disciplines. They will bring internationally recognised expertise in statistics and methodological leadership in digital trials, demonstrated by a strong record of publications and contributions to the design and delivery of complex, large-scale clinical and population health studies.  The post will directly lead and manage a team of Research Scientists, along with providing wider senior leadership and direction via matrix management within the study. 

This is a full-time post (35 hours per week), and you will be offered a fixed-term contract until 31/08/2029.

About You

To be successful in this role, we are looking for candidates to have the following skills and experience:

Essential criteria

  • PhD in statistics, biostatistics, epidemiology, health data science, or other closely related disciplines
  • Proven internationally recognised leadership in the development and implementation of innovative statistical and methodological approaches, particularly in design and conduct of AI-enabled or digital trials within large-scale observational and interventional population health studies
  • Extensive experience in clinical epidemiology, with a strong track record of applying epidemiological principles to real-world healthcare data and study design
  • Strong publication record in high-quality peer-reviewed journals, relevant to digital health, statistics, clinical trials, and epidemiology
  • Success in securing substantial competitive research funding, with experience in leading or contributing to core methodological components of major grant proposals
  • Familiarity with regulatory, ethical, and governance frameworks relevant to digital health research and AI-enabled studies (e.g. GDPR, GCP, and AI ethics)
  • Excellent leadership, mentoring, and communication skills, with the ability to mentor diverse research teams and engage effectively across academic, clinical, and industry sectors 

Desirable criteria

  • Experience contributing to multi-site or international collaborative research programmes, particularly in maternal, perinatal, or population health
  • Experience applying machine learning or AI methods in healthcare research, particularly within digital trials or real-world data studies
  • Expertise in analysing complex digital health data, including wearable sensor data, mobile health survey, and electronic health records
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