Torchora
Back to Home

Key Responsibilities and Required Skills for Bioinformatics Scientist

💰 $90,000 - $150,000

BioinformaticsGenomicsLife SciencesData Science

🎯 Role Definition

As a Bioinformatics Scientist you will design, implement, and maintain reproducible bioinformatics and computational genomics solutions that enable discovery across next-generation sequencing (NGS), transcriptomics, single-cell, and multi-omics projects. You will partner with wet-lab scientists, clinicians and cross-functional product teams to translate biological questions into robust computational analyses, deliver high-quality results, and continuously improve pipelines, data models, and interpretation frameworks. This role requires hands-on experience with NGS analysis (alignment, variant calling, RNA-seq, single‑cell), strong programming in Python and/or R, workflow orchestration, and experience with cloud or HPC environments.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Bioinformatics Analyst or Bioinformatics Engineer
  • Computational Biologist (entry/mid-level)
  • Research Associate with computational genomics experience

Advancement To:

  • Senior Bioinformatics Scientist
  • Lead/Principal Scientist, Computational Genomics
  • Bioinformatics Team Lead or Manager

Lateral Moves:

  • Data Scientist (life sciences / healthcare)
  • Genomics Software Engineer
  • Computational Genomics Product Manager

Core Responsibilities

Primary Functions

  • Design, implement and maintain end-to-end NGS analysis pipelines (DNA-seq, WGS/WES, RNA-seq, targeted panels) using reproducible workflow languages (e.g., Nextflow, Snakemake, WDL) to ensure scalable, traceable genomics processing in cloud and HPC environments.
  • Lead development and optimization of variant calling and genotyping workflows (GATK, FreeBayes, DeepVariant), including best-practice preprocessing (BWA/HISAT2/STAR alignments, duplicate marking, base recalibration) and comprehensive benchmarking against truth sets.
  • Build, validate, and maintain structural variant, copy-number, and indel detection pipelines across short- and long-read sequencing platforms (Illumina, PacBio, Oxford Nanopore), including integration of hybrid assembly approaches and long-read aligners.
  • Develop and apply transcriptomics analyses for differential expression, fusion detection, alternative splicing, and transcript isoform quantification (DESeq2, edgeR, Salmon/Kallisto, STAR-Fusion), supporting both bulk and single-cell RNA-seq datasets.
  • Design and perform single-cell RNA-seq, ATAC-seq and multi-omics analyses using state-of-the-art toolkits (Seurat, Scanpy, ArchR), including data normalization, clustering, cell-type annotation, trajectory inference and differential expression.
  • Implement robust quality-control (QC) frameworks and automated QC reporting (fastqc, MultiQC, custom checks), interpret QC metrics, and coordinate remediation or reruns with wet-lab teams to ensure data integrity.
  • Architect and maintain reproducible analysis environments using containerization (Docker, Singularity) and Infrastructure as Code or deployment automation for cloud (AWS, GCP, Azure) and on-prem HPC clusters.
  • Integrate and curate genomic annotation resources (Ensembl, RefSeq, GENCODE, ClinVar, COSMIC) and implement variant annotation pipelines (VEP, ANNOVAR) for functional interpretation and clinical reporting.
  • Lead the design and validation of variant prioritization and interpretation frameworks for disease-relevant variants, incorporating population frequency, conservation, in silico predictions, and clinical databases to produce actionable variant reports.
  • Collaborate with statisticians and data scientists to design, implement and validate statistical models and machine learning approaches for biomarker discovery, phenotype prediction, and multi-omics integration using scikit-learn, TensorFlow, or PyTorch as appropriate.
  • Implement scalable data storage, indexing and query solutions for large genomics datasets (BAM/CRAM, VCF, HDF5/AnnData), design efficient data models, and liaise with data engineering to ensure performant access for downstream analysis and visualization.
  • Produce reproducible analysis reports and visualizations (R Markdown, Jupyter, ggplot2, matplotlib, interactive dashboards) and translate computational results into clear summaries for scientists, clinicians, and stakeholders.
  • Establish, document and enforce best-practice standards for version control (Git), metadata management, and pipeline testing (unit/integration tests, continuous integration) to ensure scientific reproducibility and regulatory readiness.
  • Lead benchmarking, validation and continuous improvement of bioinformatics methods, evaluating new tools and algorithms against established metrics and integrating upgrades with minimal disruption to production pipelines.
  • Mentor and train junior bioinformaticians, coordinate code reviews, and foster a collaborative environment focused on reproducibility, transparency and technical excellence.
  • Partner closely with wet-lab scientists to design experiments and sample selection strategies that maximize information content for downstream computational analyses and to troubleshoot laboratory or sequencing issues.
  • Contribute to preparation of internal documentation, SOPs, and external deliverables such as manuscripts, conference abstracts, and grant proposals that present computational methods and biological insights.
  • Ensure compliance with data governance, privacy and security policies (HIPAA, GDPR where applicable), including secure access control, de-identification strategies, and proper handling of controlled-access genomic data.
  • Support clinical and translational pipelines by implementing quality systems and traceability for variant interpretation workflows, and collaborating with clinical teams to deliver reports and evidence summaries for decision-making.
  • Monitor and manage project timelines, prioritize competing demands across multiple studies, and communicate technical status, risks and mitigation plans to project leads and stakeholders.
  • Develop and maintain APIs, reproducible R/Python packages and internal libraries to standardize common genomic analyses, accelerate team productivity and reduce duplicated effort.
  • Evaluate and integrate public and proprietary datasets (GTEx, TCGA, UK Biobank, internal cohorts) to expand analytical context and robustness of discovery and validation analyses.

Secondary Functions

  • Support ad-hoc data requests and exploratory data analysis.
  • Contribute to the organization's data strategy and roadmap.
  • Collaborate with business units to translate data needs into engineering requirements.
  • Participate in sprint planning and agile ceremonies within the data engineering team.
  • Assist with procurement and evaluation of sequencing, storage and compute resources aligned to project demand.
  • Represent the bioinformatics team in cross-functional working groups and contribute to technical standards and architecture discussions.

Required Skills & Competencies

Hard Skills (Technical)

  • Strong programming in Python (pandas, numpy, pysam) and R (Bioconductor, tidyverse); experience producing reproducible, well‑documented code and packages.
  • Hands-on NGS analysis expertise: alignment (BWA, HISAT2, STAR), variant calling (GATK, FreeBayes, DeepVariant), and post-processing tools (samtools, bcftools).
  • Workflow management and reproducibility: practical experience with Nextflow, Snakemake, WDL/Cromwell, and CI/CD for pipelines.
  • Containerization and environment management: Docker, Singularity, Conda; creation and maintenance of reproducible analysis environments.
  • Cloud and HPC experience: deploying and optimizing genomics workflows on AWS/GCP/Azure and working with SLURM/PBS or cloud-native runners.
  • Variant annotation and interpretation tools: VEP, ANNOVAR, SnpEff; integration of ClinVar, gnomAD, dbSNP and custom annotation sources.
  • Single-cell and multi-omics toolkits: Seurat, Scanpy, Monocle, ArchR and related normalization/clustering/visualization techniques.
  • Statistical analysis and machine learning: hypothesis testing, regression models, clustering, dimensionality reduction, and experience with scikit-learn, TensorFlow, or PyTorch where applicable.
  • Data engineering basics: SQL, experience with large file formats (BAM/CRAM, VCF, HDF5/AnnData), indexing and efficient data retrieval patterns.
  • Version control and collaboration: Git, GitHub/GitLab, code review practices, and test-driven development for scientific code.
  • Familiarity with clinical bioinformatics standards, data privacy regulations (HIPAA/GDPR), and principles for producing clinically actionable reports.
  • Experience with visualization and reporting tools: R Markdown, Jupyter Notebooks, Plotly, ggplot2, interactive dashboards for stakeholders.

Soft Skills

  • Strong written and verbal communication skills to translate complex computational findings into clear, actionable insights for scientific and non‑technical audiences.
  • Collaborative mindset: demonstrated ability to work cross-functionally with wet-lab scientists, clinicians, data engineers and product managers.
  • Problem-solving orientation: proactive identification of bottlenecks and creative implementation of solutions in pipeline design or analysis approaches.
  • Attention to detail and rigor in statistical analyses, code reviews, documentation and versioning to maintain scientific reproducibility.
  • Project management and prioritization skills to balance multiple studies, deadlines and stakeholder expectations.
  • Mentoring and leadership: ability to guide junior staff, run training sessions and promote engineering best practices within the team.
  • Adaptability and continuous learning: staying current with evolving genomics methods, NGS technologies and computational tools.
  • Ethical judgement and confidentiality: handling sensitive genomic and patient data with discretion and aligned with governance policies.

Education & Experience

Educational Background

Minimum Education:

  • MS in Bioinformatics, Computational Biology, Genetics, Genomics, Computer Science (with relevant biology), Biostatistics or related quantitative discipline.

Preferred Education:

  • PhD in Bioinformatics, Computational Biology, Genomics, Genetics, Systems Biology or closely related field preferred for senior/scientific leadership roles.

Relevant Fields of Study:

  • Bioinformatics / Computational Biology
  • Genomics / Genetics
  • Computer Science / Software Engineering (with life science applications)
  • Biostatistics / Statistics
  • Systems Biology / Molecular Biology

Experience Requirements

Typical Experience Range:

  • 2–8+ years for Bioinformatics Scientist roles; senior roles typically require 5+ years of relevant hands-on experience.

Preferred:

  • Demonstrated experience shipping production-grade genomics pipelines, publishing peer-reviewed computational biology research or contributing to clinical interpretation workflows.
  • Prior work with clinical or translational genomics programs, oncology or rare disease genomics, and exposure to regulatory or compliance frameworks when applicable.
  • Proven track record of cross-functional collaboration, mentoring junior staff, and delivering high-impact genomic analyses that supported research findings or product features.
Explore More Careers