$156,900 – $247,000
Listed on Merck’s own careers site. You apply with them directly — we never stand between you and the employer.
What this role is
A senior scientific leadership role directing the analytical characterization of biologic drugs, suitable for an experienced analytical chemist or biochemist ready to lead a team while developing advanced testing methods for complex protein-based medicines.
Our summary, not Merck’s wording. The full posting is on their site.
Skills this role names
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What they ask for
Required
- Ph.D. in analytical chemistry, biochemistry or related field with minimum 8 years pharmaceutical industry experience (or equivalent with M.S./B.S. and additional years)
- Extensive experience analyzing biologics using chromatography and electrophoresis techniques
- Experience developing process and impurity assays using molecular biology and immunoassay methods
- Strong background in analytical control strategy development
- Demonstrated mentoring and matrix management experience
- Initiative, creativity and innovation in problem solving
- Understanding of Critical Quality Attributes, Quality by Design, Design of Experiments and statistical methods
- Ability to design and direct development of release, characterization and stability assays
- Team player with excellent verbal and written communication
- Authoring and reviewing technical reports and regulatory submissions
- Ability to deliver complex objectives under aggressive timelines
Nice to have
- Understanding of protein degradation mechanisms
- Experience with assay qualification, validation and transfer to regulated GxP environments per ICH Q2 and USP standards
- Experience with complex glycan analysis
- Experience with multiple biologic modalities such as monoclonal antibodies, antibody-drug conjugates and fusion proteins
- Knowledge of antibody-drug conjugate method development
- Scientific reputation with publications and external presentations
- External network for competitor and vendor benchmarking
- Experience with high-throughput and data-rich experimentation