Principal Scientist, Genomics & AI-Driven Target Discovery

Gsk; Stevenage; On-site; GBP 80,000 - 120,000; Full time

Job summary

GSK is seeking an innovative Principal Scientist in Genomic Technologies, Human Genetics and Genomics, to join the Genomic Technologies team. The role involves integrating computational and statistical methods to interpret genetics data and guide drug discovery, across UK, US, or Germany sites.

You will lead development of methods, collaborate with cross-functional teams, and stay at the forefront of statistical genetics and machine learning in translational sciences.

Qualifications

• PhD or equivalent in a relevant scientific discipline.
• Experience with large-scale biobank data such as UK Biobank.
• Strong programming skills in R or Python and reproducible research practices.
• Experience evaluating, improving, and developing methods for analysis of genetic data.
• Excellent communication and teamwork skills.

Responsibilities

• Evaluating, improving, testing, and developing production implementations of best-in-class methods for analysis of genetic data.
• Generating large-scale analysis outputs and tools for dynamically analysing, interpreting, and visualising genetic and genomic data.
• Collaborating with partners across teams to influence portfolio and pipeline decisions.
• Staying informed about recent research and potential applications within GSK.
• Contributing to a culture of innovation, quality, and continuous improvement.

Skills

Statistical genetics

R & Python

Genomics data analysis

Machine learning

Data visualization

Communication skills

Team collaboration

Education

PhD in Genomics

Tools

Docker

SQL

PySpark

BigQuery

Nextflow

Job description

GSK is seeking an innovative Principal Scientist in Genomic Technologies, Human Genetics and Genomics, to join the Genomic Technologies team. The role involves integrating computational and statistical methods to interpret genetics data and guide drug discovery, across UK, US, or Germany sites.

You will lead development of methods, collaborate with cross-functional teams, and stay at the forefront of statistical genetics and machine learning in translational sciences.