Genomics & Multimodal Data Scientist

BEPC Inc. - Business Excellence Professional Consulting; South San Francisco (CA); Hybrid; USD 62,000 - 76,000; Full time

Benefits offered by this job

Medical insurance

Dental insurance

Vision insurance

Life insurance

Job summary

BEPC Inc. is seeking a highly motivated Computational Scientist to join our client’s site in South San Francisco, CA. This role will support the development and application of analytical approaches to integrate and interpret genetic, genomic, and clinical data.

The candidate will have a strong hands-on background in genetic epidemiology, statistical genetics, or computational biology, with experience in GWAS, RNA‑Seq, scRNA‑Seq and multimodal data integration.

Qualifications

• PhD or MS in statistical genetics, computational biology, bioinformatics, or a related field.
• Extensive experience with large-scale genetic/genomic data analyses (GWAS, RNA-Seq, scRNA-Seq, scATAC-Seq).
• Fluency in R and Python; shell scripting.
• Experience with Git and HPC environments (SLURM).
• Strong communication and teamwork skills; ability to work with minimal supervision.

Responsibilities

• Collaborate with scientists to analyze large genetic/genomic/clinical datasets.
• Develop analytical methods to integrate and interpret data for disease biology insights.
• Implement machine learning approaches to relate imaging and omics data.
• Coordinate intake and preparation of new datasets for analysis.
• Document analyses, code, and findings; present to cross-functional teams.

Skills

R

Python

Shell scripting

Git

HPC

Communication

Teamwork

Independent work

Education

PhD or MS in Statistical Genetics / Computational Biology / Bioinformatics

Tools

SLURM

Job description

BEPC Inc. is seeking a highly motivated Computational Scientist to join our client’s site in South San Francisco, CA. This role will support the development and application of analytical approaches to integrate and interpret genetic, genomic, and clinical data.

The candidate will have a strong hands-on background in genetic epidemiology, statistical genetics, or computational biology, with experience in GWAS, RNA‑Seq, scRNA‑Seq and multimodal data integration.