Senior Bioinformatics Data Scientist (Genomics & AI)

BioNJ Inc.; New Jersey; Hybrid; USD 110,000 - 170,000; Full time

Job summary

Legend Biotech in Somerset, NJ is seeking a Scientist/Sr. Scientist, Bioinformatics Data for the Early Drug Development team.

You will design scalable computational pipelines to analyze high‑dimensional genomic data and translate findings into actionable insights for researchers and clinicians. Responsibilities include analyzing RNA‑seq, scRNA‑seq, WES/WGS and multi‑omics data, applying ML methods, ensuring reproducibility with versioned code, integrating public and internal databases, and

Qualifications

• Ph.D. or MS in Bioinformatics, Computational Biology, Data Science, Genomics, Biology, or a quantitative field.
• Proven experience handling genomic data (NGS data analysis).
• Experience with AI applications in data science.
• Strong proficiency in R and/or Python.
• Experience with standard bioinformatics tools and single-cell toolkits.

Responsibilities

• Analyze RNA-seq, scRNA-seq, WES/WGS, and multi-omics data to identify biomarkers and disease mechanisms.
• Apply statistical, ML, and data-mining techniques to extract patterns from complex data.
• Perform QC, normalization, and batch-correction across datasets.
• Design, optimize, and maintain scalable bioinformatics pipelines.
• Ensure reproducible analyses with version-controlled code.
• Integrate public and internal databases with internal datasets.
• Translate computational findings into actionable biological narratives for researchers.
• Collaborate with IT and other functional groups.

Skills

Genomics data analytics

R & Python programming

AI in data science

Critical thinking

Communication skills

Education

Ph.D. or MS in Bioinformatics/Computational Biology

Tools

Bioconductor

Seurat/Scanpy

Docker

AWS/GCP

Pandas/NumPy/Scikit-learn

Job description

Legend Biotech in Somerset, NJ is seeking a Scientist/Sr. Scientist, Bioinformatics Data for the Early Drug Development team.

You will design scalable computational pipelines to analyze high‑dimensional genomic data and translate findings into actionable insights for researchers and clinicians. Responsibilities include analyzing RNA‑seq, scRNA‑seq, WES/WGS and multi‑omics data, applying ML methods, ensuring reproducibility with versioned code, integrating public and internal databases, and