Associate Scientific Manager - Genomics

Role & responsibilities

You are the right person in our team if you have:

• A good understanding and hands-on experience omics data analysis, mathematical modeling, network construction and Big data
• Hands on-experience on diverse and large-scale data analysis with emphasis on drawing insights from large genomics datasets
• Experiences in developing pipeline for identifying mutations for phenotype improvement via genome-scale metabolic reconstruction

We need you to have:

• M. Tech/MSc. or Ph.D. in Bioinformatics/ Biochemistry/ Omics/ Molecular Biology, with at least 2 years of industrial or research experience, capable of executing projects independently
• Should have demonstrated technical expertise in computational pipeline development

To excel in this job, you must bring 3-5 years with M.Sc. or 2 Years of industrial experience with PhD with experience in:

• DNA/RNA sequence data handling and analysis including FASTQ quality assessment, Alignment, Variant Calling and Annotation.
• Handling of BWA, GATK, BCFtools, Mutect2, VCFTools, IGV, VEP/ANNOVAR tools
• RNA-Sequencing data analysis including FASTQ quality assessment, Alignment, Transcript quantification, normalization and Differential Expression Analysis.
• Excellent command on Python, R and its open source libraries with troubleshooting abilities.
• Good exposure to the UNIX environment and cloud computing resources such as GCP, AWS is a must.
• Exposure to GitHub, Conda environments would be considered as a plus.

Preferred candidate profile

Hands-on experience with:

• STAR/HISAT2, HTSeq/RSEM/StringTie, edgeR/DESeq2 tools
• genomics data analysis such as Methylation, CNV, single-cell RNA sequencing

The candidate must have a good understanding of:

• elementary concepts of genetics (chromosome, gene, allele, transcript, CNV, SNP/InDel). 
• Next-generation sequencing platforms and its applications in healthcare. 
• Variant Filtration and Prioritization
• genomic databases and their annotations (For example: ClinVar, GenBank, RefSeq, ENSEMBL, dbSNP,  UCSC Genome Browser, gnomAD,1000G, etc). 
• open source drug discovery databases (For example TCGA, ICGC, COSMIC, cBioPortal, CCLE etc)
• parametric and non-parametric statistical methods, hypothesis testing would be considered as a plus.

If you can join us immediate, then kindly share your resume at rupant.naik@ics.innoplexus.com