Scientist (Statistical Genomics)
Join us and contribute to the discovery of medicines that will impact lives!
About Aganitha
Accelerate drug discovery and development for Biopharma and Biotech R&D with in silico solutions leveraging Computational Biology & Chemistry, High throughput Sciences, AI, ML, HPC, Cloud, Data, and DevOps.
In silico solutions are transforming the biopharma and biotech industries. Our cross-domain science and technology team of experts embark upon and industrialize this transformation. We continually expand our world-class multi-disciplinary team in Genomics, AI, and Cloud computing, accelerating drug discovery and development. What drives us is the joy of working in an innovation-rich, research-powered startup bridging multiple disciplines to bring medicines faster for human use. We are working with several innovative Biopharma companies and expanding our client base globally. Read about how and what solutions we build.
Aganitha (अगणित): “countless” or “limitless” in Sanskrit serves as a reminder and inspiration about the limitless potential in each one of us. Come join us to bring out your best and be limitless!
Role Overview
You will lead the design, analysis, and interpretation of population genomics studies using large-scale datasets (WES/WGS/eQTL/pQTL), contributing directly to disease research and therapeutic development.
Key Responsibilities
- Design and execute population genomics studies for rare and complex disease research leveraging multi-modal integrative analyses
- Well versed with NGS data analyses- WGS/ WES data, variant annotation and interpretation, QTL analyses (eQTL, pQTL, mQTL, sQTL)
- Genetic association analyses (GWAS) and downstream analyses (fine-mapping, LD mapping, causal gene-phenotype correlation, Mendelian randomization)
- Strong statistical background- regression, glm, MDR, PCA prediction modelling, Bayesian statistics, SVM
- Prior experience in tools like PLINK, GCTA, ANNOVAR, samtools, bcftools, GATK
- Build and apply pipelines for:
- Variant calling and quality control
- Genotype imputation and fst statistics
- Haplotyping and linkage disequilibrium analysis
- Variant annotation and gene-aggregate testing (SKAT, burden tests, etc.)
- Polygenic risk score (PRS) modelling- PRSice, LDPred2
- Utilize and interpret data from genomic and transcriptomic resources such as gnomAD, ClinVar, GWAS Catalog, and PRS Catalog
Required Qualifications
- Ph.D. in Genomics, Statistical Genetics, Bioinformatics, or a related discipline (or equivalent industry experience)
- Strong understanding of population genetics, statistical modeling, and genomic data interpretation
Preferred Qualifications
- Programming experience in Python and/or R
- Experience working in HPC environments or cloud platforms (AWS, GCP, Azure)
- Prior exposure to complex and rare disease genomics or building optimised pipelines for translational research workflows