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5d ago

Bioinformatics Data Scientist

Spectrix Analytical Services, LLC Cambridge, MA

Cambridge, MA

5d ago

$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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1mo ago

Senior Scientific Data Engineer

Lawrence Berkeley National Laboratory San Francisco Bay Area, CA

San Francisco Bay Area, CA

1mo ago Closed

$139k - $235k/yr Full time Python Senior Apache Airflow

This position is no longer accepting applications (closed Jun 29, 2026).

Job Description

The Senior Scientific Data Engineer at Lawrence Berkeley National Laboratory, working with the Joint Genome Institute (JGI), will design and operate core scientific data systems, data workflows, and AI-ready pipelines, emphasizing data management, job orchestration, and platform integration. This role is based in the San Francisco Bay Area with a hybrid work arrangement.

Responsibilities

Develop and enhance JGI's core scientific data and compute capabilities as part of a skilled engineering team.
Design, implement, and deploy production automated systems, APIs, and workflows that support genomic data movement, metadata management, job orchestration, data access, and large-scale scientific computing.
Identify and resolve technical issues and integration gaps while driving ongoing system improvements.
Strengthen the reliability, scalability, observability, interoperability, and maintainability of shared production data systems while supporting sustainable operations and delivery.
Promote engineering best practices through technical reviews, knowledge sharing, and team process optimization.

Requirements

A Bachelor's Degree (or equivalent knowledge/training) in Computer Science or a related field and a minimum of 8 years of related professional experience developing, integrating, deploying, and operating production software and data systems that support metadata management, workflow orchestration, data lifecycle operations, and broad user data access or an equivalent combination of education and professional experience.
Strong knowledge of software and data engineering fundamentals relevant to data-intensive distributed systems, including system design, concurrency, performance, and testing.
Experience with database and data storage technologies including relational databases, object storage, and systems for managing structured, semi-structured, and large-scale data.
Experience with data engineering and event-driven technologies such as Airflow or Kafka.
Experience effectively using AI coding agents such as Claude Code, Codex, Cursor, including demonstrated judgment in reviewing and validating generated software for correctness, quality, security, maintainability, and suitability for production use.
Proficiency in Python and experience with one or more additional programming languages.
Excellent communication skills, including experience organizing and presenting complex technical information to internal teams and stakeholders.
Demonstrated ability to work effectively with users, stakeholders, and engineering teams to deliver technical results in a complex, interdisciplinary environment.

Technologies

Python
Airflow
Kafka
Claude Code
Codex
Cursor
WDL
Nextflow

Benefits

Exceptional health and retirement benefits, including pension or 401K-style plans
A culture where you’ll belong; we are invested in our teams
Winter Holiday Shutdown every year
Parental bonding leave (for both mothers and fathers)
Pet insurance
Relocation assistance

Desired Qualifications

A Master’s Degree (or equivalent knowledge/training) in Computer Science or a related field.
Experience working with genomics, bioinformatics, and/or next-generation sequencing data.
Experience with scientific workflow languages or workflow systems such as WDL and Nextflow.
Experience with full-stack or front-end application development.
Experience working in High Performance Computing (HPC) environments.

Additional Information

Application Date: Priority consideration will be given to candidates who apply with a resume and cover letter by June 1, 2026. Applications will be accepted until the job posting is removed.
Appointment Type: Full time, exempt from overtime pay, monthly paid, two-year term appointment with benefits eligibility, with potential for extension or conversion to a Career appointment based on performance, funding, and operational needs.
Salary Range: Budgeted salary range is $139,440 to $174,312 annually, within the broader range of $139,440 to $235,308 for job code C71.3; final offer depends on qualifications and experience.
Background Check: The position is subject to a background check; convictions will be evaluated for relevance to the role. A prior conviction does not automatically disqualify an applicant.
Work Modality: Hybrid work schedule with on-site presence at 1 Cyclotron Road, Berkeley, CA 94720; residency within 150 miles is required; some cases of full-remote work may be considered. Real ID or equivalent identification is required.
Relocation Assistance: Eligible for relocation support.
Work Authorization: Applicants must be legally authorized to work in the United States. Berkeley Lab does not provide visa sponsorship for this position.
Misconduct Disclosure Requirement: Finalists must disclose any misconduct-related administrative or judicial decisions within the last seven years; disclosure is a condition of employment.

Who is JGI?

The Joint Genome Institute (JGI) is a global leader in genome science, enabling biological discovery through advanced genomic capabilities, expert support, and large-scale, AI-ready data resources. As a DOE Office of Science user facility supported by BER, JGI advances BER's mission to achieve a predictive understanding of complex biological, Earth, and environmental systems to support the nation’s energy and infrastructure needs.

Why join Berkeley Lab?

Exceptional health and retirement benefits, including pension or 401K-style plans
A culture where you belong; we are invested in our teams
Winter Holiday Shutdown every year
Parental bonding leave (for mothers and fathers)
Pet insurance

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2mo ago

Lead Scientific Data Engineer

Lawrence Berkeley National Laboratory San Francisco Bay Area, CA

San Francisco Bay Area, CA

2mo ago Closed

$159k - $268k/yr Full time Python Apache Airflow Big Data

This position is no longer accepting applications (closed Jun 21, 2026).

Job Description

Join Lawrence Berkeley National Laboratory’s Joint Genome Institute in the San Francisco Bay Area with hybrid work options, a supportive culture, and a competitive compensation package. This role offers a salary range of USD 158,808 to 267,996 per year, strong health and retirement benefits, and a commitment to work-life balance and professional growth.

We are seeking a Lead Scientific Data Engineer to provide senior technical leadership for core genomic data systems, data management, job orchestration, and platform integration that drive AI enabled scientific discovery.

Responsibilities

Provide senior technical leadership for JGI's core scientific data and compute platforms by crafting implementation roadmaps, data system architectures, and long term strategy.
Design and implement production automated systems, APIs, and workflows that enable genomic data movement, metadata management, job orchestration, data access, and large scale scientific computing.
Improve reliability, scalability, observability, interoperability, and maintainability of shared production data systems while supporting sustainable operations and delivery.
Collaborate with product managers, scientists, and users to drive cross team alignment and integration decisions that address complex technical challenges and shared priorities.

Requirements

Bachelor’s degree in Computer Science or a related field and a minimum of 12 years of professional experience with large scale scientific data and compute infrastructures, or an equivalent combination of education and experience.
Proven experience leading the design, development, integration, and operation of production software and data systems that support metadata management, workflow orchestration, data lifecycle operations, and broad user data access.
Advanced knowledge of data and software engineering fundamentals relevant to data intensive distributed systems, including system design, concurrency, performance, and testing.
Wide experience with databases and data storage technologies, including relational databases, object storage, and systems managing structured, semi-structured, and large scale data.
Experience with data engineering and event driven technologies such as Airflow or Kafka.
Strong experience using AI coding agents like Claude Code, Codex, or Cursor, with the ability to review and validate generated software for production quality, security, and maintainability.
Proficiency in Python and experience with one or more additional programming languages.
Excellent communication skills, with the ability to present complex technical information to diverse audiences.
Demonstrated ability to lead through influence in interdisciplinary environments, aligning users, stakeholders, and engineering teams around shared requirements and implementation plans.

Technologies

Airflow
Kafka
Claude Code
Codex
Cursor
Python
WDL
Nextflow

Benefits

Exceptional health and retirement benefits, including pension or 401K style plans
A belonging culture with strong investment in team wellbeing and growth
Vacation and sick time plus a Winter Holiday Shutdown each year
Parental bonding leave for both mothers and fathers
Pet insurance

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Bioinformatics Data Scientist

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