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Postdoctoral Fellow – Large Molecule Discovery – LMD
| Company | Amgen |
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| Location | Thousand Oaks, CA, USA |
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| Salary | $85099 – $93064 |
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| Type | Full-Time |
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| Degrees | PhD |
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| Experience Level | Entry Level/New Grad |
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Requirements
- A doctorate degree (PhD) in biochemistry or analytical chemistry.
- A working understanding of mass spectrometry, and its application for peptide and protein analytics in a dynamic research environment.
- A working understanding of Python programming.
- A working understanding of molecular dynamics packages.
Responsibilities
- Conduct & contribute to the design and execution of research projects.
- Analyze data and prepare scientific manuscripts to assist Amgen’s pipeline.
- Develop novel and biopharmaceutical industry leading peptide, protein, antibody and multi-specific antibody MS and MS/MS analytical techniques.
- Create automated sample handling, digestion, data processing and computational workflows that will accelerate Amgen’s therapeutic pipeline.
Preferred Qualifications
- A doctorate PhD in biochemistry or analytical chemistry, with a focus on mass spectrometry analytical and computational methods.
- A deep understanding of mass spectrometric, intact and sub-unit protein RPLC-MS analyses, bottom-up RPLC-MS/MS approaches, other protein structural analytical methods and techniques for post translational modifications (PTMs) and high order structure determination and inference will be advantageous.
- Extensive experience with HDX-MS, FPOP, X-linking or other covalent labelling techniques will be advantageous.
- A good understanding of protein digestion methods, sample handling, MS and LC automation and application is highly desired.
- Experience with Agilent LC-ToF/Q-ToF MS, Thermo Orbitrap QExactive and Fusion Tribrid MS and Waters Q-ToF instruments are highly desired.
- Experience handling and analyzing large-scale MS, MS(MS) and/or proteomics datasets using statistical and machine learning techniques is desirable.
- A good working knowledge and experience with scientific computing libraries such as NumPy, pandas, SciPy, or scikit-learn will be advantageous.
- A working understanding of molecular dynamics, such as Molecular Operating Environment (MOE) and/or Schrodinger is preferred.
- Familiarity with running software in cloud computing environments is highly preferred.
