Bioinformatician (Cancer Science Institute, GeDac)

National University of Singapore; Singapore; On-site; SGD 70,000 - 100,000; Full time

Job summary

A national research university in Singapore is seeking a skilled Genomic Data Scientist/Bioinformatician to join their Genomics and Data Analytics Core. This role focuses on developing bioinformatics workflows for RNA-sequencing analyses, collaborating with engineers and investigators. Candidates should possess a Master's or Ph.D. in relevant fields and demonstrate proficiency in Python or R with experience in high-throughput genomic data processing. This position offers an exciting opportunity to work on cutting-edge research initiatives that could advance biology and precision medicine.

Qualifications

• Proven track record of processing and analyzing high-throughput sequencing data at scale.
• Strong problem-solving abilities to manage multi-thousand sample datasets.
• Commitment to producing high-quality, reproducible work.

Responsibilities

• Collaborate to design and optimize cloud-based components for genomic datasets.
• Lead advanced transcriptomic investigations and RNA analyses.
• Provide expert bioinformatics consultation to research teams.

Skills

Python

R

High-Performance Computing

Cloud computing (AWS)

Version control systems (Git)

Education

Master's or Ph.D. in Genetics, Genomics, Bioinformatics, Computational Biology, or Data Science

Tools

Nextflow

Snakemake

WDL

Job description

Company description:

The National University of Singapore is the national research university of Singapore. Founded in 1905 as the Straits Settlements and the Federated Malay States Government Medical School, NUS is the oldest higher education institution in Singapore.

Job Description

The Cancer Science Institute of Singapore (CSI) is seeking a collaborative and highly skilled Genomic Data Scientist/Bioinformatician to join our Genomics and Data Analytics Core (GeDaC). Our facility is powered by a multi-disciplinary team of cloud engineering experts who are establishing a robust, petabyte-scale infrastructure on AWS for the automated processing and analysis of large-scale cancer genomics datasets.

This role is specifically designed for a scientist who excels at building state-of-the-art bioinformatics workflows for RNA-sequencing analyses. You will leverage our production-grade "AI Factory" to move beyond standard pipelines, exploring the intricate complexities of the transcriptome at a massive scale to advance biological discovery and precision medicine.

Key Responsibilities

Working collaboratively with software engineering experts, principal investigators, and research teams, the successful candidate will:

• Large-Scale Data Operations: Collaborate with a strong engineering team to design and optimize cloud-based components capable of processing and analyzing genomic datasets spanning thousands of patients.
• Intricate RNA Analysis: Lead advanced transcriptomic investigations, with a particular focus on alternative splicing, transcript discovery, RNA editing, and RNA modifications.
• Platform & API Development: Help design biologically-sensible APIs for genome analytics to be utilized by research teams.
• Scientific Collaboration: Provide expert bioinformatics consultation to CSI/NUS investigators, communicating complex results clearly to diverse audiences.
• Strategic Initiatives: Participate in state-of-the-art machine learning, AI, and big data projects driven by institutional and national initiatives.

Requirements

Education & Experience

• Master's or Ph.D. in Genetics, Genomics, Bioinformatics, Computational Biology, or Data Science.
• Proven track record of processing and analyzing high-throughput sequencing data (NGS) at scale, with an emphasis on large-scale RNA-seq or single-cell cohorts.

Technical Skills

• Programming Proficiency: Strong proficiency in Python and/or R.
• Computational Infrastructure: Familiarity with High-Performance Computing (HPC) and/or cloud computing environments (AWS preferred).
• Reproducibility: Understanding of version control systems (Git) and reproducible research practices.

Professional Attributes

• Strong problem-solving abilities and the analytical thinking required to manage multi-thousand sample datasets.
• Commitment to producing high-quality, reproducible work within collaborative, multidisciplinary teams.

Preferred Qualifications

• Specialized RNA Expertise: Demonstrated experience in analyzing splicing, transcript discovery, RNA editing, or RNA modifications.
• Workflow Mastery: Familiarity with workflow management systems such as Nextflow, Snakemake, or WDL.
• Data Management: Understanding of database systems and data management practices.
• Scientific Impact: Authorship on peer-reviewed scientific publications showcasing large-scale genomic analysis.