Computational Scientist

Position Title: Computational Scientist

Work Location: 1 DNA Way, South San Francisco, CA 94080

Assignment Duration: 12 Months

Work Arrangement: Onsite

Work Schedule: 40 hours per week, 8 hours per day

Position Summary:

Multimodal perturbation datasets are becoming increasingly large and available, and understanding which data contribute most to model performance is an increasingly important question.

The contractor will train and evaluate models on this data to characterise how different portions of the dataset contribute to model learning, and will evaluate model performance and model outputs against experimental data.

Contract Highlights:

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  Full time, 40 hours per week, for an initial term of 12-months.

  
  
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  Ownership of a clearly scoped, business-critical workstream with defined deliverables.

  
  
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  Direct collaboration with the project lead and access to an established, documented analysis codebase and cluster compute.

  
  
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  Work with some of the most talented people in the biotechnology industry.

  
  

Key Responsibilities:

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  Train and evaluate models on multimodal perturbation datasets.

  
  
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  Characterise how different portions of the dataset contribute to model learning.

  
  
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  Evaluate model performance and model outputs against experimental data.

  
  

Qualification & Experience:

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  PhD in a computational discipline, or equivalent research experience.

  
  
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  Preferred majors: Computational Biology, Computational Sciences, Statistics or Machine Learning.

  
  
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  Training and evaluating deep learning models in PyTorch, including designing and executing controlled train/test split experiments on a SLURM cluster.

  
  
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  Strong applied statistics.

  
  
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  Proficient scientific Python (numpy, pandas, scanpy/anndata) and reproducible, documented analysis practice.

  
  

Preferred Qualifications:

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  Experience with single-cell transcriptomics.

  
  
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  Experience with analysis of CRISPR perturbation screens (perturb-seq or similar).

  
  
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  Familiarity with representation learning.

  
  
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  Familiarity with image analysis.