Computational Scientist

Apollopros; South San Francisco (CA); On-site; USD 96,000 - 165,000; Full time

Job summary

Apollopros is seeking a Computational Scientist (Contract) in South San Francisco, onsite, for a 12-month engagement. The role focuses on applying advanced machine learning and statistics to complex biological datasets, with emphasis on model evaluation against experimental outcomes.

You will train and optimize deep learning models using large multimodal perturbation data, design controlled experiments on SLURM clusters, and analyze results with stakeholders.

Qualifications

• Education PhD in a computational discipline or equivalent research experience.

Responsibilities

• Train, optimize, and evaluate deep learning models using large-scale multimodal perturbation datasets.
• Design and execute controlled train/test split experiments to assess data subsets impact on learning and predictive performance.
• Analyze model outputs and compare findings against experimental biological data.
• Develop quantitative frameworks to understand data value, model performance, and generalization capabilities.
• Conduct rigorous statistical analyses and communicate results to project stakeholders.
• Utilize existing analysis pipelines, computational infrastructure, and cluster computing resources to execute research objectives.
• Maintain reproducible, well-documented analytical workflows and scientific code.

Job description

Computational Scientist (Contract) Location: South San Francisco, CA (Onsite) Duration: 12-Month Contract Schedule: Full-Time, 40 Hours per Week Advance Cutting-Edge Computational Biology Research

What You'll Do

As a Computational Scientist, you will leverage advanced machine learning and statistical methods to analyze complex biological datasets and evaluate model performance against experimental outcomes.

Key Responsibilities

• Train, optimize, and evaluate deep learning models using large-scale multimodal perturbation datasets.
• Design and execute controlled train/test split experiments to assess how specific subsets of data contribute to model learning and predictive performance.
• Analyze model outputs and compare findings against experimental biological data.
• Develop quantitative frameworks to understand data value, model performance, and generalization capabilities.
• Conduct rigorous statistical analyses and communicate results to project stakeholders.
• Utilize existing analysis pipelines, computational infrastructure, and cluster computing resources to execute research objectives efficiently.
• Maintain reproducible, well-documented analytical workflows and scientific code.

Why This Opportunity?

• Full-time contract position with an initial 12-month engagement.
• Ownership of a clearly defined, high-visibility research project with measurable deliverables.
• Direct collaboration with project leadership and experienced computational scientists.
• Access to established analytical frameworks, documented codebases, and high-performance computing resources.
• Opportunity to contribute to innovative research alongside leading professionals in computational biology, machine learning, and biotechnology.

Required Qualifications

• Education PhD in a computational discipline or equivalent research experience.
• Preferred Fields of Study Computational Biology Computational Sciences Statistics Machine Learning Related quantitative disciplines.
• Technical Requirements Proven experience training and evaluating deep learning models using PyTorch.
• Experience designing and executing controlled machine learning experiments on SLURM-based computing clusters.
• Strong foundation in applied statistics and quantitative analysis.
• Advanced proficiency in scientific Python, including: NumPy Pandas Scanpy AnnData Experience building reproducible, documented, and scalable data analysis workflows.

Preferred Qualifications

• Candidates with the following experience are especially encouraged to apply: Single-cell transcriptomics analysis.
• CRISPR perturbation screening analysis, including Perturb-seq or similar methodologies.
• Representation learning and advanced machine learning techniques.
• Biological or scientific image analysis.
• Experience working with large-scale biological datasets and computational genomics workflows.

Ideal Candidate

The ideal candidate combines deep technical expertise in machine learning and scientific computing with a strong research mindset. You are comfortable working independently, designing rigorous experiments, and translating complex computational results into meaningful scientific insights. You thrive in collaborative research environments and are excited by opportunities to advance the understanding of biological systems through data-driven approaches.

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