Bioinformatics/Data Scientist (Computational Biology, EDDC)

Agency for Science, Technology and Research (A*STAR); Singapore; On-site; USD 60,000 - 100,000; Full time

Job summary

An innovative research platform is seeking a Bioinformatics/Data Scientist to join their dynamic Computational Biology group. This role focuses on driving data-driven discoveries in drug development, utilizing advanced machine learning techniques and multimodal data analytics. You will collaborate with a diverse team of scientists to analyze complex biological datasets and uncover meaningful insights for therapeutic targets. The ideal candidate will have a PhD in a relevant field and experience in multi-omics data analysis. Join a forward-thinking organization dedicated to tackling significant medical challenges and advancing healthcare solutions.

Qualifications

• PhD in Computational Biology or related field with strong data analytics focus.
• 2+ years of postdoc experience or equivalent in industry settings.

Responsibilities

• Develop computational methods for analyzing biological datasets.
• Collaborate with scientists to prioritize therapeutic targets and biomarkers.

Skills

Python

R

Machine Learning

Bioinformatics

Data Analytics

Multi-Omics Data Analysis

Education

PhD in Computational Biology

Postdoc Experience

Tools

Cloud-based Database Services

APIs

Workflow Development Frameworks

Job description

Overview: At EDDC, Singapore’s national platform for drug discovery and development, we are committed to discover and develop novel therapeutics by working collaboratively with both public sector and industry partners to translate innovations from bench to clinic using industry relevant and cutting-edge technology platforms. This includes tackling complex and unmet medical challenges in disease areas such as oncology, inflammatory and autoimmune diseases. EDDC is now seeking a highly skilled and motivated Bioinformatics/Data Scientist to join the Computational Biology group to drive data- driven discovery of therapeutic targets and clinically relevant biomarkers, achieving wholistic understanding of human disease biology. By joining EDDC, the candidate will be part of a dynamic, interdisciplinary team geared to enhance and facilitate the drug discovery journey through data harmonization and deconvolution, multimodal data analytics, and advanced machine learning. The role will also leverage strategic partnerships both locally and globally, to adopt a flexible approach in refining and advancing EDDC’s solutions.

Responsibilities:

Multimodal data analytics for target, biomarker discovery and MoA elucidation

• Develop and apply computational methods to analyze diverse biological datasets, including genetics/genomics, transcriptomics (bulk, single-cell, and spatial RNAseq) and proteomics data.

• Leverage machine learning and AI techniques to uncover meaningful patterns and build predictive models to support target identification and mechanism-of-action studies.

Collaborative Research and strategic partnerships

• Collaborate with biologists, chemists, and other scientists to prioritize novel therapeutic targets and biomarkers, design experiments, analyze results, and communicate findings in scientific reports and presentations.

• Support and contribute to collaborations with external partners in computational biology and AI, ensuring alignment with internal organizational and project goals.

Innovation and Continuous Learning

• Stay abreast of the latest developments in bioinformatics, data science, machine learning, and AI as they pertain to drug discovery and development.

• Implement new technologies and methodologies to refine in-house workflows and enhance EDDC’s computational platforms.

Requirements:

• PhD in Computational Biology, Bioinformatics, Computer Science, or a related field, with a strong emphasis on data analytics and machine learning in biological contexts. Minimally 2 years of postdoc experience or equivalent in industry settings.

• Demonstrated experience in multi-omics data analysis (eg. genetics/genomics, bulk, single-cell, and spatial transcriptomics, proteomics, etc.) and the application of machine learning in biological research.

• Proficiency in Python and R, and familiarity with cloud-based and on-premises database services, APIs and workflow development frameworks.

• Excellent communication and collaboration skills, with experience working independently and in multi-disciplinary teams.

• Keen to learn new techniques and adaptable to changing priorities.

• Proficiency in machine learning and AI methodologies for analyzing large-scale biological data, with a proven track record of developing predictive models to guide therapeutic development and strategy is an advantage.

• Industry experience in pharmaceutical or biotech companies is an advantage.