Analyst [GC-MS Data Interpretation Chemist]

3 weeks ago

Bengaluru Karnataka India, Karnataka TÜV SÜD Full time

Key Responsibilities:


  • Interpret GC-MS data (primarily EI) for identification of unknowns using spectral deconvolution and comparison to library databases for chemical characterization studies.
  • Evaluate spectral quality, match scores, and fragmentation patterns using a weight-of-evidence approach, including critical assessment of database matches.
  • Apply knowledge of retention indices and chromatographic behavior to confirm compound identity.
  • Collaborate with technical analysts, chemists, and project managers to communicate findings, resolve discrepancies, and meet client expectations.
  • Ensure data integrity and compliance with regulatory and accreditation standards (e.g., ISO 10993, ISO 17025, GLP).
  • Document findings in established systems, supporting traceability and reporting.
  • Contribute to method development and optimization by providing feedback on spectral data trends and anomalies.
  • Support improvement of SOPs, templates, and data analysis workflows.
  • Identify and troubleshoot potential prep or instrument-related issues affecting data integrity.
  • Participate in continuous improvement initiatives and inter-laboratory collaborations.
  • Other duties as assigned by management.


Additional Knowledge/Qualifications:


  • BS/B.Sc in Chemistry or a related field with 5+ years relevant experience, or MSc./PhD with 2+ years of relevant experience.
  • Proficient in interpreting GC-MS and chromatographic data.
  • Experience working in a regulated environment (ISO 17025, ISO 10993, GLP/GMP).
  • Familiarity with Microsoft Excel for data documentation and review.


Preferred Candidate:


  • Background in organic chemistry, including fragmentation patterns under EI/CI, volatility, retention index behavior.
  • Experience with extractables & leachables, particularly in a medical device or pharmaceutical context.
  • Understanding of instrumental analysis workflows and solution preparation.
  • Awareness of how sample prep or instrument anomalies affect data outcomes.
  • Familiarity with relevant databases and software for compound identification.

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