Scientist I/II, Biological Data Science

Direct Hire Fulltime Role

Salary Range: $124,800 - $145,000 per year

Location: South San Francisco, CA - Onsite Role

Job Summary:

The BDS Scientist will work closely with experimental biologists to assist in experimental design and be responsible for downstream bioinformatic/statistical analysis of various high-throughput screens/assays.

This position requires ambition, adaptability, and a collaborative mindset, but also a strong ability to organize parallel projects and work independently to establish analysis pipelines in uncharted research territories.

Responsibilities:

• Work closely with experimental biologists and bioengineers on experimental design and analytical approaches to achieve strategic engineering/therapeutic objectives.
• Analyze high-throughput data generated by wet lab scientists, including CRISPR screens, bulk/scRNA-seq, bulk/scATAC-seq, Perturb-seq, and modulator screens.
• Independently design and implement bespoke analysis pipelines for in-house screens.
• Communicate analysis results clearly and effectively cross-team to both experimental scientists as well as non-scientists

Required Qualifications:

• Ph.D. in Bioinformatics, Computational Biology, Genetics/Genomics, Bioengineering, Computer Science, or Statistics, with 0-5 years of experience in industry/academia; alternatively
• MS in one of the above fields with 5+ years experience in industry/academia or BS in one of the above fields with 10+ years experience in industry/academia
• Experience analyzing high-throughput sequencing datasets (e.g., CRISPR screens, RNA-seq, ATAC-seq, ChIP-seq, WGBS, scRNA-seq, etc.)
• Strong programming background in Python or R (preferably both)
• Demonstrated ability to communicate research results effectively to non-data scientists
• Experienced in experimental design and working collaboratively cross-team with experimental scientists.

Preferred Skills:

• Expertise in gene regulation, epigenetics, CRISPR biology, or gene therapy
• Background in protein engineering (involving model-guided approaches to protein design and molecular dynamic simulations)
• Theoretical knowledge and practical experience in machine learning/deep learning
• Lentiviral vector design, packaging, transduction, and cell line creation
• Familiarity with AWS or other cloud computing infrastructures
• Familiarity with GitHub

 

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