Lead Clinical Assay Developer

5 days ago

Singapore beBeeMolecularBiologist Full time
Scientist Role">

Lucence is a precision oncology company committed to bringing clarity to cancer care. They make ultrasensitive non-invasive liquid biopsy tests to provide doctors and their patients with life-changing information for earlier detection and effective treatment.

Headquartered in Palo Alto and Singapore, Lucence supplies personalized cancer care services through twin CLIA-licensed laboratories in the United States and Singapore.

As a Development Scientist, you will play a vital role in developing and implementing clinical assays to advance cancer care. The role involves designing and conducting laboratory experiments, planning and leading assay validation studies, troubleshooting technical challenges, driving automation of novel assays, maintaining experimental records, preparing detailed protocols and validation reports, and training staff and facilitating assay transfer from R&D to clinical operations.

Key Responsibilities
  • Design and conduct laboratory experiments to develop new clinical assays.
  • Plan and lead assay validation studies.
  • Troubleshoot and resolve technical challenges during assay development.
  • Drive the automation of novel assays.
  • Maintain accurate and organized experimental records.
  • Prepare detailed protocols and validation reports.
  • Train staff and facilitate assay transfer.
Requirements

To be a good fit for this role, you should have:

  • A PhD in Molecular Biology, Cancer Biology, Genomics, or related field.
  • A proven track record of quick learning.
  • Strong background in molecular biology, including PCR, RT-PCR, genotyping, and NGS.
  • At least 2 years of working experience in the development of clinical assays, with significant hands-on expertise in troubleshooting, optimizing, and validating molecular assays.
Benefits

The following would be advantageous but not required:

  • A deep understanding and knowledge of oncology and/or genomics.
  • Proficiency in R or Python.
  • Fluency in variant annotation and interpretation, and use of genome databases such as COSMIC, ClinVar, Varsome, Ensembl, and OncoKB.
  • Ability to interpret genomic data in patient- and treatment-specific contexts.

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