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Computational Biologist / Bioinformatics Workflow Developer

Recruitment.aiUnited States🇺🇸United StatesPosted 14 Aug 2026

Quick Overview

Work Type

Remote

Level

Mid Senior

Job Description

Position: Computational Biologist / Bioinformatics Workflow Developer

Location: Remote

Key Responsibilities

Data Analysis & Integration: Perform comprehensive analysis of multi-modal datasets, including RNA-seq, epigenetics, GWAS, proteomics, and perturbational data at bulk and single-cell levels
Workflow Development: Translates best in class algorithmic approaches into reproducible and efficient bioinformatics workflows in standardized workflow languages (Nextflow, WDL).
Statistical Modeling: Customize libraries for multivariate statistical analyses, machine learning models, and survival analysis to identify predictive signatures and biological signals.
Pipeline & Tool Development: Complement and extend existing codebases to generate specific visualizations, or develop new computational methods to address limitations in current approaches.
Cross-Functional Communication: Effectively convey complex data analysis results to diverse audiences, including scientists, product owners, and software developers, through platforms like Jupyter or R Notebooks as well as in-person presentations.
Collaboration & Communication: Synthesize complex computational results into actionable insights and clear visualizations to be presented to interdisciplinary teams and internal stakeholders.
Technical Documentation: Ensure high standards of reproducibility by thoroughly
documenting code, analysis processes, and research findings.

Education & Experience

Education: Bachelor''s degree or higher in Computational Biology, Systems Biology, Bioinformatics, Genomics, Immunology, Statistics, or a related quantitative field.
Industry Experience: 2–5 years of relevant experience dealing with large-scale biological and clinical datasets.

Technical Skills

Workflow Languages: You have expert knowledge of a modern bioinformatics workflow language (WDL, Nextflow, CWL, or snakemake) and have deployed workflows in HPC or cloud environments.
Programming: Expert-level proficiency in R (including packages like ggplot2 and Bioconductor) and/or Python (specifically Scanpy, AnnData, Pandas, and scikit-learn).
Computational Environment: Proficiency using Linux-based servers, high-performance computing (HPC), and version control systems like Git.
Artificial Intelligence: Experience in agentic coding workflows or broadly leveraging AI/LLM tools to accelerate development and data analysis.
Data Stewardship: Familiar with FAIR (Findable, Accessible, Interoperable, Reusable) data principles, with experience using containerization technologies (e.g., Docker, Singularity) to create reproducible and portable computational environments. environments.

Skills

Docker

Machine Learning

Scikit-learn

Git

Jupyter

LLM

Pandas

Python

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