Bioinformatics

Bioinformatics Data Jobs

4 jobs found

Today

Bioinformatics Data Scientist

Spectrix Analytical Services, LLC Cambridge, MA

Cambridge, MA

Today

$60+/hr Contract Python Amazon Web Services Analytics

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Job Description

Contribute to drug discovery and translational research by enabling computational proteomics, multi-omics analytics, scalable data workflows, and AI driven scientific analyses.

Responsibilities

Examine large scale proteomics and multi-omics data to support biomarker discovery, pharmacodynamic analysis, pathway and causal inference, disease biology, and drug discovery programs.
Design scalable, reproducible, cloud‑ready analytical workflows, data pipelines, reports, dashboards, APIs, and data products for scientific users.
Utilize statistical modeling, machine learning, and AI/LLM powered approaches to enhance biological interpretation, knowledge extraction, workflow automation, and scientific decision support.
Merge proteomics data with orthogonal modalities such as transcriptomics, genomics, genetics, perturbation data, metadata, and translational annotations.
Partner with computational scientists, mass spectrometry specialists, discovery biologists, translational researchers, and data science teams to define analytical strategies and clearly communicate results.
Champion data QC, reproducible analysis, workflow development, software engineering standards, and responsible use of AI‑assisted scientific tools.

Requirements

Doctorate in bioinformatics, computational biology, data science, statistics, computer science, systems biology, biology, biochemistry, chemistry, engineering, or a related quantitative field.
Hands-on experience with high‑dimensional biological or biomedical data such as proteomics, LC‑MS, Olink, SomaScan, transcriptomics, single‑cell data, spatial omics, genomics, genetics, or other omics modalities.
Strong proficiency in R and/or Python for data analysis, statistical modeling, visualization, and reproducible scientific computing.
Solid understanding of experimental design, data QC, normalization, missing-value assessment and imputation, feature selection, and advanced statistical modeling for high‑dimensional data, including linear and mixed‑effects models, Bayesian methods, biological signal deconvolution, pathway interpretation, and communicating findings.
Experience with scalable computing and cloud environments, including AWS services and workflow‑based analysis systems for large scientific datasets.
Proficiency with machine learning, AI, and LLM powered analytics, including developing robust agentic or multi‑agent frameworks for scientific workflows.
Familiarity with drug discovery, translational research, biomarker discovery, perturbation biology, pharmacodynamic studies, disease biology, or related biomedical contexts.
Ability to work independently on complex analytical problems and convey results clearly to scientific stakeholders.

Technologies

R
Python
AWS
Nextflow
Snakemake
Airflow
Docker
AWS Batch
ECS
DIA-NN
Spectronaut
MaxQuant
FragPipe
Proteome Discoverer
Skyline
Shiny
Dash
Streamlit
FastAPI
Flask
Posit/RStudio
Posit Connect
Claude
Parquet
SQL
Git
Olink
SomaScan

Benefits

401(k)
401(k) matching
Dental insurance
Employee assistance program
Flexible schedule
Flexible spending account
Health insurance
Health savings account
Life insurance
Paid time off
Parental leave
Professional development assistance
Referral program
Retirement plan
Tution reimbursement
Vision insurance

Preferred Qualifications

Postdoctoral, industry, or equivalent applied research experience after PhD.
Direct experience with computational proteomics across platforms such as LC‑MS proteomics, phosphoproteomics, DIA/SWATH, DDA, TMT, label‑free quantification, PTM analysis, spectral library generation and prediction, and affinity‑based proteomics such as Olink or SomaScan.
Hands‑on experience with proteomics software, outputs, or data structures from tools like DIA‑NN, Spectronaut, MaxQuant, FragPipe, Proteome Discoverer, Skyline, or similar platforms.
Experience building reusable scientific workflows, analytical pipelines, applications, dashboards, APIs, reports, or self‑service data products for scientists.
Experience deploying analytical workflows or data products on AWS or comparable clouds using Nextflow, Snakemake, Airflow, Docker, AWS Batch, ECS, or equivalent systems.
Experience delivering R and Python software and data products with Posit/RStudio, Posit Connect, Shiny, Dash, Streamlit, FastAPI, Flask, or similar platforms.
Strong software engineering practices including Git, modular code, documentation, testing, and reproducible workflow development.
Hands‑on experience building LLM enabled applications or workflows using Claude or other large language models, including RAG systems, tool‑using agents, prompt engineering, evaluation frameworks, LLMOps, or scientific knowledge extraction.
Experience applying ML or foundation-model approaches to biological data, including representation learning, multimodal modeling, classification/regression, embedding‑based retrieval, generative AI, or related methods.
Experience with data modeling, SQL, Parquet, metadata management, data lake architectures, or large scale biological data warehouses.
Strong publication record, open source contributions, or demonstrated delivery of reusable computational tools, analytical platforms, scientific workflows, or production quality internal data products.

Strong Differentiators

Ability to bridge computational proteomics, biological interpretation, cloud engineering, bioinformatics methodology development, and AI/ML/LLM workflow implementation for life science applications.
Proven success building tools, analytical methods, or platforms adopted by experimental, translational, or computational scientists.
Contributions to peer reviewed publications in bioinformatics, computational biology, proteomics, machine learning, systems biology, or related fields.
Experience designing AI assisted, machine learning, or agentic workflows that are reproducible, traceable, scientifically reliable, and suitable for biological and biomedical research.
Strong understanding of connecting omics data, pathway biology, perturbation data, genetics, and drug discovery questions into reusable analytical systems.
Ability to develop and publish novel analytical methodologies when appropriate.
Track record applying AI and ML techniques to life science datasets, including biomarker discovery, target identification, predictive modeling, knowledge extraction, or multi‑omics integration.
Ability to help shape future scientific AI strategy beyond executing predefined analyses.

Experience

Bioinformatics: 1 year (Preferred)

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4d ago

Data Scientist II

Axle Rockville, MD

Rockville, MD

4d ago

$130k - $145k/yr Full time Python Analytics Anndata

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Job Description

Axle is seeking a Data Scientist II to support the National Cancer Institute (NCI) at the NIH in Rockville, MD, with a salary range of USD 130,000 to 145,000 per year.

Responsibilities

Design, implement, and maintain reproducible bioinformatics pipelines for diverse biomedical data types including genomic, transcriptomic, single-cell, spatial, proteomic, metagenomic, metabolomic, and clinical datasets; develop reusable transformation logic and curated datasets for analytics, dashboards, APIs, notebooks, and downstream research workflows.
Collaborate on multi-omics analyses for NCI CBIIT labs, covering bulk RNA-seq workflows (quality control, differential expression, gene set enrichment analysis), single-cell RNA-seq (clustering, UMAP/t-SNE visualization, cell type annotation, DEG), and Digital Spatial Profiling tasks (annotation, QC, normalization, spatial deconvolution, volcano plots, heatmaps).
Enable reliable data movement from source systems into analysis-ready formats; support ingestion, curation, metadata capture, source-to-target mapping, schema management, provenance tracking, and long-term maintainability of data products.
Apply statistical methods and machine learning techniques to biomedical data, including hypothesis testing, regression, clustering, dimensionality reduction (PCA, UMAP, t-SNE), and classification; incorporate AI or LLM-based extraction where appropriate with clear validation and stakeholder communication.
Develop and support researcher-facing applications and visualizations, including interactive dashboards (Shiny, Streamlit), notebooks, reports, and APIs to explore multi-omics and clinical data; assist with figure generation for QC, differential expression, pathway, and spatial analyses.
Collaborate with data scientists, bioinformaticians, researchers, developers, and government stakeholders to translate scientific needs into technical specifications, data models, and reusable workflows that accelerate biomedical research.

Requirements

Education and background: Bachelor's degree in Data Science, Bioinformatics, Computer Science, Biological Sciences, or a related field; advanced degree preferred, or equivalent experience. Demonstrated experience in data-intensive roles supporting biomedical research or scientific computing.
Data science and bioinformatics expertise: Proficiency in Python and R for analysis, scripting, and visualization; hands-on experience with at least two omics data types such as bulk RNA-seq, scRNA-seq, spatial transcriptomics, proteomics, metagenomics, or GWAS.
Analytical skills: Strong foundation in statistical modeling, dimensionality reduction, clustering, differential expression analysis, and pathway analysis; experience with structured, semi-structured, and unstructured data across relational and data lake environments.
Collaboration and communication: Excellent problem-solving abilities with the ability to communicate across technical and non-technical audiences; capable of translating scientific needs into technical solutions and clearly articulating risks, assumptions, and limitations.
Domain alignment: Genuine interest in biomedical and translational research; ability to quickly learn domain terminology and workflows, with awareness of data governance, privacy, and compliance requirements for clinical and research data.

Technologies

Python, R, Shiny, Streamlit, Snowflake, Databricks, Galaxy, Terra, Nextflow, WDL, Snakemake, Singularity, CWL, Scanpy, Squidpy, SCIMAP, AnnData, Jupyter, Posit Connect, AWS, EC2, S3, Lambda, SLURM, SGE, NIH Biowulf

Benefits

100% Medical, Dental & Vision Coverage for Employees
Paid Time Off and Paid Holidays
401K match up to 5%
Educational Benefits for Career Growth
Employee Referral Bonus
Flexible Spending Accounts: Healthcare, Parking Reimbursement Account, Dependent Care Assistant Program, Transportation Reimbursement Account

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6d ago

Research Data Scientist

US Veterans Health Administration Salt Lake City, UT

Salt Lake City, UT

6d ago

Full time Analytics Bioinformatics Clinical Data

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Job Description

In the offices of the U.S. Veterans Health Administration, a Research Data Scientist is positioned to advance microbiome research within the Office of Research and Development. This onsite role in Salt Lake City blends bioinformatics processing with collaborative analysis to support studies aimed at enhancing Veterans’ care through data-driven insights.

Responsibilities

Process raw microbiome sequencing data using established bioinformatics pipelines
Perform sequence quality control, taxonomic and functional profiling, and prepare analysis-ready feature tables
Conduct routine statistical summaries, including diversity calculations, simple group comparisons, and preliminary visualizations to support downstream modeling by senior team members
Assist with transferring, merging, and validating microbiome outputs within the secure VA computing environment, ensuring consistency with clinical datasets prepared by the statistics team
Work closely with the Senior Statistician to troubleshoot data issues, adjust preprocessing steps as needed, and produce preliminary figures and descriptive results for internal discussions
Contribute to the preparation of study reports, abstracts, and manuscript supplements by generating tables and visual materials
Support ongoing project needs by implementing minor analytical tasks, updating pipelines as new samples or methods become available
Coordinate with investigators to ensure microbiome data products align with project requirements

Requirements

You must be a U.S. Citizen to apply for this job
Selective Service Registration is required for males born after 12/31/1959
Proficiency in written and spoken English
Subject to background and security investigation
Selected applicants will be required to complete an online onboarding process; acceptable forms of identification are required for pre-employment documentation
Effective May 7, 2025, driver’s licenses or state IDs not REAL ID compliant cannot be used as acceptable identification for employment
Participation in the seasonal influenza vaccination program is a requirement for all Department of Veterans Affairs Health Care Personnel
Complete all application requirements detailed in the "Required Documents" section of this announcement

Technologies

QIIME 2
MetaPhlAn 4
Python
R

Benefits

Comprehensive benefits package
Access to federal employee benefits

How You Will Be Evaluated

Evaluation based on how well you meet the stated qualifications; responses should be specific and verifiable, with references to your application materials
Experience includes paid and unpaid work, including volunteer service; all qualifying experience will be considered
VA pre-employment reference checks are employed to verify information and assist in selection
Policy supports offering opportunities to individuals who have faced financial hardship or unemployment
Veterans and transitioning service members have access to VA career tools and reintegration support

Agency Contact Information

VHA National Recruitment Center
Phone: (844) 456-5208
Email: VHANationalRecruitmentCenter@va.gov
Address: Salt Lake City ORD VISN 19; 550 Foothill Dr; Salt Lake City, UT 84158; US

Next Steps

After you submit your online application, you will receive a confirmation email. Applications, including all required documents, will be reviewed after the posting closes to verify qualification and eligibility. If eligible, a referral certificate will be issued and applicants will be notified by email; referred candidates may be contacted directly for further steps. You can check your application status anytime by logging into your USA Jobs account. Additional information is available in the USAJobs Help Center.

Overview

Accepting applications
Posted this month · Apply in 2 days
Due by 11:59 p.m. ET on July 15, 2026
Location: Salt Lake City, UT
Telework eligible: No

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18d ago

Senior Bioinformatics Data Engineer

Kaztronix Wilmington, DE

Wilmington, DE

18d ago

Full time Senior Bioinformatics Data

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Job Description

This senior data and platform engineering role focuses on accelerating biomarker data lake modernization on AWS, building and operating production pipelines across oncology clinical studies.

Responsibilities

Build and maintain Dagster‑driven ingestion pipelines for genomics vendors (Caris, Predicine, Tempus, Olink, CellCarta) with IO managers, Iceberg writers, and row‑level accounting.
Hardene dbt transformations from Silver to Gold, including real‑data test coverage, store‑failures patterns, staging/intermediate/mart models, and macro consolidation.
Implement clinical data ingestion paths for SDTM and ADaM, add reconciliation logic, and route subject‑dimension data.
Deliver platform infrastructure such as FastAPI endpoints, CI/CD pipelines, containerized deployments, observability instrumentation, and Redshift performance tuning.
Extract transformation rules from legacy R and PySpark code and align them with new platform implementations.
Identify repetitive processes and convert them into automated workflows, guardrails, or reusable tooling.
Participate in adversarial design and code reviews, identify edge cases, and push back on suboptimal patterns.
Collaborate with the lead engineer on design decisions and sustain delivery velocity through paired sessions and PR reviews.
Ensure reproducibility standards: CI on every PR, automated tests, and avoid ad hoc notebook‑based production processes.

Requirements

AI native engineering practice: proven experience building systems and workflows around AI coding agents (Claude Code, Cursor, Codex, or equivalent) beyond prompting; know when to automate repetitive tasks, add guardrails for agent outputs, and build infrastructure to accelerate future work.
Education: Bachelor's or master's degree in computer science, data engineering, bioinformatics, or a related field.
Experience: 5+ years in data engineering with shipped production pipelines on AWS (S3, ECS/Fargate, Redshift or equivalent MPP).
Programming: strong Python and SQL skills with working knowledge of modern data engineering libraries.
Orchestration and dbt: advanced proficiency with dbt and a workflow tool (Dagster, Airflow, or Prefect).
Data quality: track record of catching silent failures, questioning data correctness, and detecting lossy joins or incomplete deliveries.
Architecture: solid understanding of lakehouse patterns, ETL processes, and schema design for complex multi‑modal datasets.
Compliance: ability to handle PHI‑adjacent clinical data under contractor policy (background check, compliance training, VPN access).
Legacy code: willingness to work with R and PySpark to extract business rules and validate new implementations.
Communication: excellent ability to collaborate in an embedded pair model with tight feedback loops.

Technologies

Dagster, dbt, Apache Iceberg, Amazon S3, Redshift
FastAPI, Docker, Amazon ECS, AWS Fargate
Python, SQL, R, PySpark
Airflow, Prefect
CloudFormation, AWS Glue Catalog, CI/CD

Preferred Qualifications

Direct experience with Apache Iceberg, AWS Glue Catalog, or lakehouse table formats.
Comfort reading genomic data (VAF, HGVS nomenclature, VCFs, CNV/fusion semantics) or ability to ramp on unfamiliar scientific domains quickly.
Familiarity with clinical data standards such as SDTM, ADaM, and CDISC.
Pharma, clinical research, or life sciences background.
Experience with containerization (Docker/ECS) and infrastructure‑as‑code (CloudFormation).
Proficiency in R for interoperability with bioinformatics teams.

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Bioinformatics Data Jobs

Bioinformatics Data Scientist

Data Scientist II

Research Data Scientist

Senior Bioinformatics Data Engineer

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