Bioinformatics Data Scientist

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)