Senior Scientist/Team Lead for Biosystem Analysis

4 weeks ago


Singapore MOJIA BIOTECH PTE. LTD. Full time
Roles & Responsibilities

  • MojiaBio is seeking a highly motivated scientists for the Biosystem Analysis team at our R&D headquarters located in Shanghai. The ideal candidates will play key roles in the development of state-of-the-art approaches to develop and improve our chemical manufacturing platforms. The positions will be tasked with the design and development of sophisticated computational models to analyze and interpret multi-omics data. The scientists will be part of MojiaBio’s Synthetic Biology (SynBio) team and will work closely with members of the protein engineering, strain engineering, bioconversion and process engineering teams.
Responsibilities:
  1. Provide strategic direction for the Biosystems Analysis team, aligning strategies with company goals, collaborating and providing value to cross-functional teams. Work within a collaborative, and interdisciplinary team to actively contribute to executing the vision of the company
  2. Lead the Biosystems Analysis team, driving innovation, growth and collaboration. Able to set priorities, allocate resources, and enable career development of the team members. Recruitment and building up the Biosystems Analysis team is a key need.
  3. Utilize state-of-the-art data mining technologies to process, analyze and interpret multi-omics data, including genomics, transcriptomics, proteomics, and metabolomics. Integrate diverse biological data sets to gain a comprehensive understanding of cellular behavior and system-level responses for experimental design and guide decision-making processes.
  4. Analysis of complex biological datasets through the design and development of sophisticated computational models, making use of statistical and machine learning methodologies to extract meaningful insights.
  5. Employ cutting-edge network analysis approaches to identify key biological pathways, regulatory interactions, and functional modules within biological systems to uncover potential targets for pathway and metabolic engineering.
  6. Effectively communicate complex analysis results and scientific findings across departments and within cross-functional teams to address complex biological questions and develop innovative solutions to biomanufacturing challenges.
  7. Manage projects and collaborations with external parties, defining project scope and deliverables, maintaining communication and keeping tasks on track.
  8. Keep up-to-date with the advancements in the field and apply emerging technologies to advance projects.
  9. Participate with IP team to protect, enrich and increase value of the company's technology IP portfolio.

Qualifications
  1. Ph.D. degree in Bioinformatics, Computational Biology, Computer Science, or related fields with a minimum of 6 years of experience working in biotech or related industries. MSc with significant relevant experience will be considered.
  2. Experience with leading a team of at least 5 people or more is a plus
  3. Excellent background in hands-on analyzing high-throughput biological data: data cleaning, functional annotation, normalization, analysis, interpretation and visualization.
  4. Experience in genomic data mining and identifying functions of potential genes via bioinformatics and/or artificial intelligence/machine learning approaches are essential.
  5. Extensive development experience with R, JAVA, C, C++, or Python languages.
  6. Strong statistics and mathematics background, experience with AI or machine learning is a plus.
  7. Must be safety-oriented, self-motivated, and highly organized individuals.
  8. Capable of adapting and working well in a collaborative, fast-paced work setting, contributing to a positive and inclusive team culture.
  9. Demonstrates excellent verbal and written communication skills in Chinese and/or English.
  10. Strong interpersonal skills, with the ability to effectively communicate and collaborate with colleagues.

Tell employers what skills you have

Machine Learning
Proteomics
Genomics
Mathematics
Career Development
Process Engineering
Biology
Data Mining
Experimental Design
IP
Bioinformatics
Statistics
Computational Biology
Visualization
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