BIOINFORMATICIAN JOB DESCRIPTION

A Bioinformatician in academic cancer research owns the computational infrastructure that turns raw sequencing output into findings investigators can publish, present, and act on. Pipelines break. They do not. Working within interdisciplinary teams that span wet-lab scientists, clinicians, and grant-funded faculty, this role maintains and advances methods for whole genome sequencing, RNA-seq, ATAC-seq, and single-cell datasets. The work is accountable to both the rigor of peer review and the timelines of institutional grant reporting cycles.

As the Bioinformatician, you design and operate computational pipelines for high-throughput sequencing data while translating complex genomics findings into analyses that directly support cancer research publications, clinical hypotheses, and grant deliverables. You work across an interdisciplinary team of experimental biologists, clinicians, and faculty investigators, with scope spanning from data ingestion and quality control through statistical modeling and results presentation.

Career Impact: Sustained work on WGS, ATAC-seq, and single-cell methods within a research institution builds a publication record and domain depth that is directly transferable to senior computational roles in academic, biotech, and pharmaceutical settings.

Business Impact: Without accurate, reproducible pipelines, sequencing data collected at significant cost cannot be validated, published, or used to inform clinical decisions, making this role the operational foundation of each research program it supports.

Growth Opportunity: Exposure to multi-modal sequencing technologies and cross-disciplinary collaboration with clinicians and faculty creates a clear path toward lead computational biologist or principal investigator roles within academic or translational research environments.

• Design and operate computational pipelines for whole genome, exome, RNA-seq, ATAC-seq, and single-cell sequencing data to meet publication and reporting standards.
• Develop and maintain analysis methods for genomic, epigenomic, and transcriptomic datasets, drawing on current scientific literature to evaluate emerging approaches.
• Validate and monitor data quality across sequencing runs, identifying problematic outputs and correcting root causes before downstream analysis.
• Collaborate with wet-lab scientists and clinicians to interpret sequencing results, design follow-on experiments, and refine biological hypotheses.
• Build and document reproducible, maintainable code for both independent projects and use by colleagues without formal computational training.
• Manage and archive large NGS datasets, maintaining detailed records of all analyses to support audit trails and reproducibility requirements.
• Present findings and progress updates at lab meetings and scientific conferences to support grant reporting and collaborative research programs.

• Bachelor's degree or higher in Bioinformatics, Computational Biology, Computer Science, or a related field, or equivalent work experience.
• 2 or more years of applied bioinformatics experience, with demonstrated work on high-throughput sequencing datasets in a research setting.
• Strong foundation in biostatistics and computational genomics, including experience developing algorithms and statistical models for sequencing data.
• Proficiency in at least one high-level programming language used in numerical and scientific computing, with ability to write clean, version-controlled code.
• Experience working within a Unix/Linux environment for pipeline execution, data handling, and automation.
• Ability to communicate findings clearly in written and verbal form to both computational and non-computational collaborators.
• Familiarity with machine learning concepts and their application to biological sequence or omics data.

• PhD in a bioinformatics-related field, with a relevant publication record demonstrating independent computational research contributions.
• Experience with multi-modal sequencing data types including spatial transcriptomics, circulating tumor DNA, or single-cell modalities beyond standard bulk RNA-seq.
• Prior experience managing or mentoring junior computational staff within a collaborative academic or research environment.
• Background in cancer biology, evolutionary genomics, or clinical genomics sufficient to contextualize analytical findings within disease research programs.

• Pipeline reproducibility rate, measured by the proportion of analyses that can be re-run with identical outputs from archived code and data.
• Data quality pass rate across sequencing runs, reflecting how consistently raw sequence output meets institutional QC thresholds before downstream analysis.
• Grant and publication milestone adherence, tracking whether computational deliverables are completed within the timelines committed to funding agencies.
• Time from raw data receipt to validated analysis-ready dataset, indicating pipeline efficiency for the research team's experimental cadence.
• Cross-disciplinary presentation frequency, measured by the number of lab meetings and conference appearances where computational results are formally communicated.
• Typical tools: Workflow management systems (commonly Nextflow or Snakemake); statistical computing environments (commonly R or Python with scientific libraries)

• Base Salary Range: $75,000 to $115,000 annually, depending on degree level and years of experience
• Bonus: Typically not standard; merit increases and supplemental grant funding common in academic settings
• Equity: Generally not applicable in academic and non-profit research institutions
• Health Benefits: Medical, dental, and vision coverage through institutional plans, often subsidized
• PTO: 15 to 25 days annually, plus institutional holidays and sick leave
• Common Perks: Tuition benefits, access to high-performance computing clusters, conference travel funding, and professional development support

Figures are estimates based on general US market benchmarks and may be outdated. Adjust based on location, company size, and seniority level.

Reasonable accommodations are available to applicants with disabilities throughout the hiring process and in the performance of job duties. All qualified applicants will receive equal consideration for employment without regard to race, color, religion, sex, national origin, age, disability, veteran status, sexual orientation, gender identity, or any other characteristic protected under applicable federal, state, or local law. Employment is contingent on successful completion of a background check. Candidates must be authorized to work in the United States.

The Bioinformatician owns the technical input for designing and implementing laboratory testing and analytic workflows, including NGS pipeline construction for infectious pathogen identification and antimicrobial-resistance mapping across a public health setting. Working alongside senior epidemiologists, clinical laboratories, and local health authorities, this role shapes the accuracy of surveillance outputs and strengthens the scientific foundation for grants, policy recommendations, and standard operating procedure updates.

Key Responsibilities

• Consult with senior epidemiologists, clinical laboratories, and local health authorities on bioinformatics analysis results and make recommendations on further scientific analysis, presentation, and publications.
• Prepare results for release by ensuring accuracy within laboratory information management systems or other reporting systems as required.
• Monitor staff compliance with established policies and procedures regarding sequencing data analysis, quality metrics review, and results reporting.
• Perform quality control activities of sequencing analysis including verification, validation, routine quality control, and proficiency testing per institutional quality management program requirements.
• Develop and apply tools for assessment of NGS data quality and bioinformatics pipelines to evaluate raw sequence quality and output data performance.
• Provide technical oversight to personnel performing sequencing analysis to ensure accurate surveillance, proper diagnosis, and improved laboratory productivity.
• Review processes and outcomes of laboratory testing and sequencing analysis and make recommendations for revision and improvement according to established protocols.

Required Qualifications

• Demonstrated knowledge of microbiology, immunology, and molecular biology concepts including microbial culture techniques, biochemical tests, serology, sequencing, and nucleic acid-based detection assays.
• Demonstrated knowledge of quality assurance practices including equipment maintenance, proficiency testing, method validation, and staff competency in diagnostic microbiology and molecular biology settings.
• Experience overseeing quality control activities and initiatives in a laboratory setting.
• Knowledge of laboratory certifications and accreditations to ensure adherence to regulatory requirements.
• Ability to coordinate microbiological sample storage, handling, specimen retention, and packaging and shipping of infectious substances.
• Ability to investigate non-conforming events and provide reports to management on turnaround time delays and proficiency test failures.
• Ability to manage multiple projects and priorities accurately and on time in a dynamic environment with shifting deadlines.
• Strong communicator with the ability to establish productive working relationships with internal and external stakeholders.
• Proficiency with Microsoft Office including Word, Excel, and PowerPoint.

Embedded within the ARGO RDPC program, the Bioinformatician delivers scalable, portable, and reproducible computational workflows that support uniform genomics data processing across the ICGC collaborative environment. Working closely with business analysts, program members, and external users, this role builds the pipeline infrastructure and metadata systems that enable reliable data submission, quality control, and accessibility at scale.

Core Functions

• Develop scalable, portable, and reproducible computational workflows to perform uniform analyses.
• Develop technical plans and implement tools for data quality control, validation, and reporting.
• Perform routine workflow execution, monitoring, and debugging for ARGO RDPC in the Collaboratory OpenStack environment.
• Perform benchmarking tests to evaluate different solutions and suggest optimizations.
• Develop fully automated workflows for routine data handling processes such as data transfer from EGA to ICGC data repository.
• Participate in collaborative code review with peers to improve code quality.
• Explore public biological databases and open source bioinformatics tools and build prototypes to evaluate integration plans.
• Interact with business analysts to assist the translation of bioinformatics needs into software functional requirements and specifications.
• Contribute to preparing and maintaining documentation for user guides, SOPs, and data processing pipelines.
• Maintain and update ARGO dictionary and metadata schemas to enhance data validation and support new data types.

Qualifications and Experience

• Undergraduate degree or higher in bioinformatics, genomics, computational biology, or computer sciences with significant bioinformatics experience.
• Extensive programming experience with Python or other languages, preferably in large-scale projects.
• Experience with bioinformatics resources, databases, tools, and common standard formats.
• Working experience analyzing large genomics datasets including raw sequencing data, gene expression, germline and somatic mutational events, copy number variants, methylation, and other data types.
• Knowledge of software development best practices including modular design, source code version control, unit testing, and continuous integration.
• Knowledge of scientific workflow systems such as CWL, WDL, Nextflow, or Snakemake is an asset.
• Experience in designing relational database schemas and knowledge of big data NoSQL solutions such as Elasticsearch and MongoDB is an asset.
• Experience with user support is an asset.
• Strong verbal and written communication skills.

Reporting to the research team lead, the Bioinformatician shapes the computational backbone of epigenetic dataset analysis, including self-reliant development and continuous improvement of software capable of processing Illumina HumanMethylation BeadChip and WGBS datasets. Partnering with project collaborators and scientific publication teams, this role delivers integrated multi-omics analyses and database management solutions that advance the organization's research outcomes.

Primary Duties

• Provide statistical and computational tools for biologically based activities such as genetic analysis, gene expression measurement, and gene function determination.
• Plan, develop, and continuously improve complex software applications capable of analyzing epigenetic datasets from Illumina iScan workflows.
• Analyze large molecular datasets including raw microarray data, genomic sequence data, and Illumina HumanMethylation BeadChip datasets across different species.
• Assess, develop, and implement strategies to integrate Illumina HumanMethylation BeadChips workflows.
• Integrate and manage large data volumes in MySQL databases.
• Coordinate projects for the integrative analysis of WGBS, ATAC-seq, and RNA-seq datasets.
• Perform analysis, documentation, and reporting on research findings.
• Prepare and support scientific publications, presentations, and posters.

Skills and Qualifications

• PhD in computational biology, bioinformatics, computational chemistry, or a closely related field.
• 3-5 years of experience in molecular biology and genetics, standard bioinformatics databases and algorithms, or pathway and protein structure and interaction databases.
• Broad hands-on experience in bioinformatics programming including shell scripting, object-oriented programming in R or Python, and RDBMS systems such as PostgreSQL and MySQL.
• Strong analytical and problem-solving skills with scientific creativity.
• Ability to work in a fast-paced, matrix, and team-oriented environment and self-motivated in leading scientific projects.
• Clear sense of organization, purpose, accountability, and concise reporting.
• Excellent verbal and written communication skills in English.

Sitting at the intersection of genomics research and national security intelligence, the Bioinformatician leads the exploitation of NGS data using approved bioinformatics tools to mine, analyze, and report on DNA sequences from environmental metagenomics samples. Operating across a multidisciplinary team at Fort Meade, this role delivers written analytical reports and database contributions that directly support program objectives in defense-relevant biological intelligence.

Duties

• Combine research in biology, medicine, and health-related studies with information technology to collect and interpret data across fields such as genetics and pharmaceutics.
• Manage, mine, visualize, and analyze biological data working closely with other professionals to achieve broader project objectives.
• Exploit identified sequences using approved bioinformatics tools and processes.
• Support incorporation of identified sequences of value into appropriate databases or repositories.
• Write reports on exploitation of DNA sequences and identification of areas of collaboration.
• Handle next-generation sequencing data and run kmer and non-kmer based analysis methods and read-mapping.

Experience and Qualifications

• 2+ years of experience performing bioinformatics work and writing reports based on resulting accomplishments.
• Strong domain knowledge in genomics with experience applying next-generation sequencing methods.
• Experience handling NGS data including kmer and non-kmer based analysis methods and read-mapping.
• Understanding of the technical underpinnings, applications, and limitations of sequencing platforms including Illumina, PacBio, and Oxford Nanopore.
• Programming skills sufficient to extract, transform, process, and clean large metagenomics datasets.
• Significant current experience in metagenomics analysis of environmental samples within the last 2 years.
• Proficiency in statistics packages such as R.
• Published reports related to genetic analysis and bioinformatics or authored new procedures to create innovative approaches to complex problems.
• Demonstrated ability to work independently as part of a multidisciplinary team and deliver timely results.

A key member of the research and development team, the Bioinformatician builds and improves production pipelines for single-cell and bulk immunoglobulin repertoire sequencing data while developing new bioinformatic tools aligned to internal priorities. Collaborating across wet-lab scientists, front-end developers, and back-end developers, this role delivers the computational infrastructure that enables effective storage, visualization, and interpretation of large immunobiology datasets.

Functions

• Extend and improve production pipelines for analyzing single-cell and bulk immunoglobulin repertoire sequencing data.
• Build new bioinformatic tools for internal research and development priorities.
• Work closely with front-end and back-end developers to enable effective storage, processing, and visualization of large and complex datasets.
• Collaborate with wet-lab scientists to interpret and design experiments.
• Model, design, test, and debug code and find creative and functional solutions to complicated problems.
• Maintain excellent documentation with impeccable attention to detail.

Background and Experience

• Graduate degree (MSc or PhD) in Bioinformatics, Computational Biology, or a related field.
• Experience working in a team environment, collaborating on solutions, and sharing code.
• Experience working with high-throughput sequencing data.
• A good foundation in statistical analysis and understanding of experimental protocols and their limitations.
• A background in immunobiology.
• Experience with machine learning techniques, feature selection, and data exploration.
• Proficiency in at least one common coding language with Python preferred.

Reliable sequencing pipeline development and regulatory-compliant software delivery depend on the Bioinformatician, who designs, configures, and implements data management processes spanning research, QC, manufacturing, and transplantation test development. Based within a biotechnology or genomics environment in Australia, this role liaises with software developers and discovery project teams to ensure sequence datasets are handled accurately and improvements are implemented to specification.

Job Functions

• Design, configure, and implement pipelines and processes for managing data from research, QC, manufacturing, and development activities.
• Assist in the development of sequencing tests for transplantation.
• Produce well-written, well-documented, and well-behaved software according to appropriate regulatory requirements.
• Lead and participate in discovery projects as required.
• Liaise with software developers to implement improvements and critical features.
• Handle large and complex sequence datasets using sequence alignment tools.

Technical Qualifications

• Degree in Molecular Genetics, Molecular Biology, or a similar field with demonstrable experience in Bioinformatics.
• Experience in biotechnology or genomics environments.
• Experience with sequence alignment tools and ability to handle large and complex sequence datasets.
• Proficiency with Unix/Linux and Java/C+.
• Good written and oral communication skills with ability to work in a team.

As the Bioinformatician, this role leads organization-wide data initiatives and operates computational pipelines for whole genome and exome sequencing, RNA-seq, ATAC-seq, single-cell, and ctDNA data analysis within an interdisciplinary cancer research team. The team relies on this work to translate high-throughput sequencing datasets into actionable clinical and scientific insights, supported by supervision of analysts and close collaboration with experimental biologists and clinicians.

Leadership Responsibilities

• Lead and own organization-wide data initiatives including models, frameworks, platforms, and dashboards.
• Supervise, coach, and mentor a high-performing team of analysts.
• Design, develop, and operate computational pipelines for the analysis of sequencing data.
• Prioritize and extract data from a variety of sources including notes, survey results, medical reports, and laboratory data and maintain its accuracy and completeness.
• Create complex charts and databases, perform statistical analyses, and develop graphs and tables for publication and presentation.
• Use system reports and analyses to identify potentially problematic data, make corrections, and eliminate root causes for data problems.
• Work with lab members to analyze biomedical data and address scientific problems with bioinformatics techniques.
• Install, maintain, and evaluate third-party software packages and manage and archive large NGS datasets.
• Present results and progress updates at lab meetings and to experimental and scientific collaborators.
• Attend seminars, lectures, and training courses to remain up to date with the cancer genomics field.

Education and Experience

• PhD in a bioinformatics-related field or Bachelor's degree or higher in Bioinformatics, Computer Science, or a related field with two years of relevant experience.
• Strong background in bioinformatics and biostatistics including analysis of high-throughput sequencing data such as WES, WGS, RNA-seq, and ATAC-seq.
• Experience with algorithm development and familiarity with machine learning, information theory, and signal processing.
• Background in cancer biology and expertise in computational genomics and evolutionary biology with a relevant publication record.
• Experience working within a Unix/Linux environment with proficiency in C, Python, and R.
• Fluency in programming languages such as Perl, Python, Java, R, C, Matlab, and MySQL.
• Experience with high-performance computing and database management.
• Ability to direct the work of others for roles requiring supervision.
• Excellent writing, analytical, and communication skills in English with ability to prioritize workload.

Bioinformatician supports an interdisciplinary cancer research team by collecting, managing, and analyzing large sequencing datasets encompassing whole genome and exome sequencing, RNA-seq, ATAC-seq, and single-cell and spatial data. The work directly supports faculty investigators, grant agencies, and clinical collaborators by transforming raw multiomics data into publication-ready analyses, statistical models, and reports that advance cancer diagnosis and treatment research.

Key Deliverables

• Collect, manage, and clean datasets from multiple sources.
• Employ new and existing tools to interpret, analyze, and visualize multivariate relationships in data.
• Create databases and reports, develop algorithms and statistical models, and perform statistical analyses appropriate to data and reporting requirements.
• Use system reports and analyses to identify potentially problematic data, make corrections, and determine root causes for data problems.
• Develop reports, charts, graphs, and tables for use by investigators and for publication and presentation.
• Collaborate with faculty and research staff on data collection and analysis methods.
• Communicate with government officials, grant agencies, and industry representatives.

Professional Experience

• Bachelor's degree or higher in Bioinformatics, Computer Science, or a related field with a minimum of one year of relevant experience.
• Strong background in bioinformatics and biostatistics including analysis of high-throughput sequencing data.
• Experience working within a Unix/Linux environment.
• Familiarity with basic molecular biology and background in cancer biology.
• Fluency in programming languages such as Perl, Python, Java, R, C, Matlab, and MySQL.
• Familiarity with machine learning, information theory, and signal processing.
• Experience with algorithm development.
• Excellent communication and team skills with fluency in spoken and written English.

The Bioinformatician builds and maintains computational tools and analysis methods that support directed evolution, protein engineering, and high-throughput screening projects aimed at developing next-generation AAV capsids across a collaborative lab environment. Working alongside wet-lab molecular biologists at the Stanley Center and Broad Institute, this role leads data-driven projects that translate complex NGS and omics datasets into actionable biological insights and peer-reviewed contributions.

Key Responsibilities

• Develop, apply, document, and maintain computational tools for own projects and to support analysis by colleagues without formal computational training.
• Design and execute data analyses to mine diverse and non-conventional datasets produced at the lab.
• Develop new analysis methods and evaluate emerging methods by following relevant scientific literature.
• Propose and lead technology-oriented data-driven projects on lab data and relevant external datasets.
• Write well-crafted, maintainable, scalable, and performant code across collaborative projects.
• Critically evaluate and interpret results and scientific merit of new technologies, new product developments, and changes to existing processes.
• Attend and present results at team meetings and communicate findings to collaborators.
• Contribute to progress reports, publications, and presentations at scientific conferences.

Qualifications and Experience

• PhD in computational biology or a related field with 2+ years of relevant industry or academic applied research experience.
• Prior project management experience and leadership of collaborative projects including experience managing or mentoring computational personnel.
• Solid foundation in molecular biology, bioinformatics, and biostatistics with experience in study and experimental design.
• Experience developing robust and generalizable computational biology methods preferably for omics and biological sequence applications.
• Fluency in scientific and numerical programming with Python and solid understanding of data science principles including statistics and algorithms.
• Foundational knowledge of machine learning with demonstrated experience developing software in a team setting.
• Demonstrated ability to communicate effectively across disciplines and facilitate productive collaborations with wet-lab scientists.
• A relevant publication record with strong initiative and ability to take ownership of complex projects.
• Ability to manage time well and respond to shifting priorities in a fast-paced environment with zealous attention to detail.