Data Jobs in Cambridge, MA

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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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