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)