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A rapidly growing company is seeking a Bioinformatics Scientist to lead bioinformatics analyses supporting genomic research and drug discovery. This role involves developing computational pipelines, analyzing large datasets, and collaborating with clinical and molecular teams to derive meaningful insights for healthcare innovations.
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Key Responsibilities:Analyze large-scale biological datasets including next-generation sequencing (NGS) transcriptomics epigenomics and proteomics.
Develop and maintain robust scalable computational pipelines for data preprocessing alignment annotation and statistical analysis.
Collaborate with molecular biologists clinicians and data scientists to support interdisciplinary research and product development.
Interpret and visualize biological data to identify patterns biomarkers or novel mechanisms relevant to human health and disease.
Maintain accurate and well-documented code protocols and analysis reports to ensure reproducibility.
Evaluate and integrate new bioinformatics tools algorithms and public datasets into research workflows.
Present research findings to internal teams and contribute to scientific publications presentations and grant proposals.
Ph.D. or Masters degree in Bioinformatics Computational Biology Genomics or a related field.
25 years of hands-on experience in bioinformatics or computational biology.
Strong programming and scripting skills (e.g. Python R Bash).
Experience with NGS technologies and tools (e.g. BWA STAR GATK DESeq2 SAMtools BEDTools).
Familiarity with biological databases (e.g. Ensembl NCBI UCSC Genome Browser).
Solid foundation in molecular biology genetics and statistics.
Strong communication skills and ability to work in a collaborative fast-paced environment.
Experience with cloud computing environments (AWS GCP) and workflow management tools (Nextflow Snakemake).
Familiarity with version control (e.g. Git) and containerization (Docker Singularity).
Background in a specific therapeutic area (e.g. oncology rare disease infectious disease).
Experience with single-cell data analysis multi-omics integration or clinical genomics.