Project Research Scientist I (AI/ML)

Ashoka University; Sonipat; On-site; INR 800,000 - 1,100,000; Full time

Job summary

Ashoka University invites applications for a Project Research Scientist I to join our genomics and multi-omics research efforts. The role focuses on statistical analysis, ML modeling, and reproducible data science workflows in R and Python across EHR and omics datasets.

The ideal candidate has a strong quantitative background, 0–2 years of experience, and is eager to contribute to manuscripts, grant proposals, and interdisciplinary collaboration with clinicians and data scientists.

Qualifications

• MSc in relevant field or BE/BTech with strong quantitative background.
• GATE/NET or equivalent exam preferred.
• 0-2 years of experience.

Responsibilities

• Perform data cleaning, preprocessing, and quality control of biomedical datasets.
• Conduct statistical analyses and ML modeling for disease risk prediction, biomarker discovery, and phenotype characterization.
• Work with large-scale multimodal datasets (EHR, omics, longitudinal cohort data).
• Implement and validate analytical workflows in R/Python with reproducibility and documentation.
• Develop predictive models, clustering approaches, and risk scores.
• Generate high-quality visualizations, reports, and presentations for research discussions.
• Collaborate with clinicians, statisticians, and data scientists.
• Maintain structured documentation of code, datasets, and results using version control.

Skills

R programming

Python programming

Statistics

Biostatistics

Data visualization

Machine learning

Data cleaning

Genomics data handling

Education

MSc in Bioinformatics / Statistics / Computer Science / Biotechnology

B.E./B.Tech in relevant field

GATE/NET or equivalent (preferred)

Tools

R

Python

Git

ggplot2

HPC / Linux

Job description

We are looking for a Project Research Scientist I who has a quantitative bend of mind, interested in statistics and programming. The project involves investigating genomics and multi-omics data to identify shared immunopathogenic events in multiplex families (multiple affected individuals) with autoimmune diseases. Specifically, this individual will:

• Perform data cleaning, preprocessing, and quality control of biomedical and clinical datasets


• Conduct statistical analyses and machine learning modeling for disease risk prediction, biomarker discovery, and phenotype characterization


• Work with large-scale multimodal datasets (EHR, omics, longitudinal cohort data) under supervision


• Implement and validate analytical workflows in R/Python, ensuring reproducibility and proper documentation


• Develop predictive models, clustering approaches, and risk scores


• Generate high-quality visualizations, summary reports, and presentations for research discussions.


• Contribute to manuscripts, grant proposals, reports, and conference abstracts


• Collaborate with interdisciplinary teams including clinicians, statisticians, and data scientists


• Maintain structured documentation of code, datasets, and results using version control and best practices


• Support ongoing grant-related research activities and project coordination when required

Qualifications

• MSc in Bioinformatics / Statistics / Computer Science/Biotechnology or a B.E./BTech in a relevant field. GATE/NET or any equivalent exam (preferred).


• Experience: 0-2 years.

Skills Required

• Intermediate to advanced proficiency in R and/or Python for data analysis and visualization


• Foundation in statistics and biostatistics (regression models, hypothesis testing, basic survival analysis)


• Knowledge of basic machine learning methods (e.g., linear/logistic regression, LASSO, Random Forest, clustering techniques)


• Ability to perform data cleaning, preprocessing, and exploratory data analysis


• Experience with data visualization tools (e.g., ggplot2, matplotlib)


• Strong analytical thinking and problem-solving skills

Desired Skills (Preferred)

• Experience handling large-scale datasets such as biobank, EHR, or longitudinal cohort data


• Exposure to advanced machine learning methods (e.g., XGBoost, Elastic Net, survival ML models)


• Basic understanding of omics data analysis (genomics, metabolomics, epigenetics, proteomics)


• Familiarity with Linux environments and/or High-Performance Computing (HPC) systems


• Experience with version control systems (Git) and reproducible research workflows


• Prior involvement in academic research projects, manuscript writing, or conference presentations


• Interest in precision medicine, and data-driven disease risk prediction


• Desirable: Hands on experience in computational genomics involving handling DNA-sequence data

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