This study underline work in maternal and child health involving pregnant mothers, infants and school children, particularly addressing the nutrition during pregnancy and body composition in infants and school children.
Project Research Scientist I (Non-Medical) [Statistician]
No. of Vacancy: One
Preferred Qualifications: MSc Statistics
Experience: 5 Years
Age Limit: 35 Years
Language: English
Salary: Rs. 72,800/- per month
Location: Bangalore
Roles and Responsibilities:
Lead Causal Inference Studies: Design and implement advanced causal models (e.g., Double Machine Learning, instrumental variables, propensity score analysis) to isolate the causal effects of specific nutritional interventions on health outcomes like glycemic response.
Develop Next-Generation Algorithms: Conduct in-depth statistical analysis of clinical and biometric data to build the core logic for personalized health recommendations, ensuring they are grounded in causal evidence.
Build & Validate Predictive Models: Develop and validate sophisticated machine learning models for prediction, while also working to distinguish correlational patterns from causal drivers in the data.
Design and Analyze Clinical Research: Shape the design of research studies and clinical trials to support robust causal claims. Execute complex statistical analyses and interpret the results to guide product development and scientific discovery.
Collaborate and Translate Findings: Partner with clinicians, dietitians, and engineers to translate complex causal questions into testable hypotheses. Clearly communicate model insights and the limitations of causal claims to technical and non-technical stakeholders.
Qualifications
Educational Background: A PhD or Master's degree in Statistics, Biostatistics, Economics,Computer Science, or another quantitative field with a strong focus on causal inference.
Advanced Causal Inference Expertise: Deep theoretical knowledge and hands-on experience implementing modern causal inference methods, specifically Double Machine Learning and/or related patterns (e.g., propensity scores, instrumental variables, regression discontinuity).
Strong Statistical & ML Foundation: Mastery of core statistical and machine learning concepts, including regression, classification, and survival analysis.
Programming Proficiency: Expertise in Python (using libraries like EconML, CausalML, Scikit-learn, Pandas) or R.
Collaborative Mindset: Excellent communication skills with a proven ability to explain complex methodologies and results to a multidisciplinary audience.
Preferred Qualifications
Interested candidates may send their Resume to:
The Principal Investigator
ICMR-CAR-MANASI Project
St. John's Research Institute
To Apply: E-mail cover letter and curriculum vitae with 3 references (names and email addresses / phone numbers) by on or before 26th September 2025 to tinku.sarah@sjri.res.in & cc to hr@sjri.res.in
For more information, please visit our website www.sjri.res.in