Aequor
Computational Scientist II
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Computational Scientist II, lasting 8 months+, with a pay rate of $55 to $61. Candidates need a PhD in a quantitative field, experience analyzing large-scale scRNA-seq datasets, and proficiency in Python and statistics. On-site work required.
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
488
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🗓️ - Date
July 8, 2026
🕒 - Duration
More than 6 months
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🏝️ - Location
On-site
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📄 - Contract
Unknown
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🔒 - Security
Unknown
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📍 - Location detailed
South San Francisco, CA
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🧠 - Skills detailed
#Data Analysis #Data Integration #Datasets #Mathematics #Statistics #Python #Computer Science #Data Science #Programming
Role description
Computational Scientist II - Onsite SSF
Duration: 8 months+
Pay rate range: $55 to $61 max
Key Responsibilities:
•
• Analyze large-scale High-Content Sequencing-based Perturbation Datasets
•
• Collaborate with interdisciplinary and cross-functional teams including biologists, chemists, data scientists, and other stakeholders.
Educational Background:
PhD degree in Quantitative field ( e.g., Computational Biology, Bioinformatics, Computer Science, Statistics, Mathematics) or with a strong Quantitative focus.
1. Proven track record of Analyzing large-scale Multi-Conditional scRNA-seq Datasets
1. Proficiency in Scientific programming in Python.
1. Strong background in Statistics, probabilistic Modeling and Data analysis.
Preferred Skills:
• Experience with the analysis of Perturb-seq/CROP-seq data, or more generally with CRISPR data.
• Multimodal data integration, in particular between multiple measurement modalities and/or clinical patient data
• Practical experience with working on HPC systems / SLURM
Computational Scientist II - Onsite SSF
Duration: 8 months+
Pay rate range: $55 to $61 max
Key Responsibilities:
•
• Analyze large-scale High-Content Sequencing-based Perturbation Datasets
•
• Collaborate with interdisciplinary and cross-functional teams including biologists, chemists, data scientists, and other stakeholders.
Educational Background:
PhD degree in Quantitative field ( e.g., Computational Biology, Bioinformatics, Computer Science, Statistics, Mathematics) or with a strong Quantitative focus.
1. Proven track record of Analyzing large-scale Multi-Conditional scRNA-seq Datasets
1. Proficiency in Scientific programming in Python.
1. Strong background in Statistics, probabilistic Modeling and Data analysis.
Preferred Skills:
• Experience with the analysis of Perturb-seq/CROP-seq data, or more generally with CRISPR data.
• Multimodal data integration, in particular between multiple measurement modalities and/or clinical patient data
• Practical experience with working on HPC systems / SLURM
