Research Assistant (Epidemiology & Evidence Synthesis)

The Company

Population Health Partners (PHP) is a prevention-focused health company that invests human and financial capital to improve healthspan for society. We operate at the intersection of life sciences, biopharmaceuticals and consumer and digital health to create high-impact companies that improve health at population scale. With offices in New York and London, PHP combines financial resources with industry-leading capabilities and technology. The firm's incubation portfolio includes Civia, Onsera and, Corsera Health.

The Challenge

This role will primarily support Corsera Health, a PHP company that is changing the face of human health—by predicting and preventing cardiovascular disease before it begins. Cardiovascular disease affects many adults and remains the leading global cause of death. Corsera brings together Nobel Prize-winning science, AI-driven prevention tools, efficient manufacturing, a disruptive business model, and a proven team of industry leaders to redefine cardiovascular wellness and empower people to live healthier, longer lives.

We are seeking a scientifically curious and highly organised Research Assistant to support epidemiological research with strong interest in quantitative evidence synthesis and meta-analysis to help build the scientific foundation underpinning our transformative precision prevention platform

The Role

You will work closely with senior epidemiologists and clinicians to design epidemiological study protocols, generate high-quality quantitative evidence through systematic reviews, meta-analyses, and pooled analyses of cardiometabolic research.

The role focuses on meta-analysis as the core analytical activity, supported by systematic review methods and applied epidemiology.

What you’ll do

Study Design & Research Planning

• Contribute to the design of epidemiological studies investigating cardiometabolic disease and prevention.
• Support the development of research protocols and statistical analysis plans.
• Help define:
  • Study populations and cohorts
  • Exposure and outcome definitions
  • Covariate selection and confounding control strategies

Evidence Synthesis & Literature Reviews

Synthesise evidence from:

• Observational cohort studies
• Randomised controlled trial (RCT) data
• Genetic epidemiology and Mendelian randomisation studies (with support from senior scientists)
• Undertake meta-analyses.
  • Conduct meta-analyses in R or Python across a range of cardiometabolic topics.
  • Implement fixed- and random-effects models.
  • Assess heterogeneity (I², τ²) and conduct subgroup analyses.
  • Perform sensitivity analyses and publication bias assessments (e.g. funnel plots, Egger tests).
  • Contribute to meta-regression and pooled effect estimation where appropriate.

Cross-Functional Collaboration

• Work closely with senior scientists, clinicians, and data scientists to ensure analytical outputs are accurate and publication-ready.
• Coordinate with internal teams on timelines for manuscript completion, review cycles, and submission.

What we offer

• The opportunity to contribute to high-impact cardiovascular research with direct real-world implications.
• Mentorship and close collaboration with leaders in cardiovascular science, causal modelling, and prevention.
• Exposure to large-scale biomedical datasets and cutting-edge AI-driven prevention science.
• Competitive salary and benefits package.

Minimum Qualifications

• MSc degree in a relevant scientific field (e.g., epidemiology, public health, biostatistics, or related).
• Understanding of epidemiological study designs (cohort, case-control, RCTs).
• Understanding of statistical methods (meta-analysis, regression, survival analysis)
• Proficiency in R or Python for data analysis and visualisation.
• Highly organised, detail-oriented, and comfortable managing multiple documents and deadlines.

Preferred Qualifications

• Experience working with biomedical, epidemiological, or cohort study datasets.
• Familiarity with cardiovascular biology, lipid metabolism, blood pressure regulation, or atherosclerosis.
• Experience using R Markdown, Python or similar tools for reproducible research.
• Familiarity with causal inference concepts and Mendelian randomisation.
• Experience with large datasets (e.g., UK Biobank, Our Future Health) is a plus.

 To apply, please click the 'Apply' button above and send a CV and 1-page cover letter to careers@populationhp.com.