Scientist/Senior Scientist, Companion Diagnostics (CDx) – IHC Development
SystImmune
Redmond, WAvia LinkedIn
SystImmune is a leading and well-funded clinical-stage biopharmaceutical company located in Redmond, WA and Princeton, NJ. It specializes in developing innovative cancer treatments using its established drug development platforms, focusing on bi-specific, multi-specific antibodies, and antibody-drug conjugates (ADCs). SystImmune has multiple assets in various stages of clinical trials for solid tumor and hematologic indications. Alongside ongoing clinical trials. SystImmune has a robust preclinical pipeline of potential cancer therapeutics in the discover and IND-enabling stages, representing cutting-edge biologics development. We offer an opportunity for you to learn and grow while making significant contributions to the company's success.
We are seeking a Scientist or Senior Scientist to lead and support immunohistochemistry (IHC) assay development for companion diagnostic (CDx) kits supporting oncology drug development. This role is responsible for end-to-end IHC assay development, including antibody selection, assay optimization, analytical validation, and clinical trial implementation, with a clear development path from RUO/LDT to IVD CDx commercialization.
The ideal candidate has strong expertise in pathology, IHC assay development, and CDx regulatory expectations, and can work effectively across R&D, clinical, regulatory, QA, and external CROs and central laboratories.
Key Responsibilities
IHC Assay & CDx Development- Lead development of IHC-based CDx assays, from biomarker discovery through assay optimization and validation.
- Select and evaluate primary antibodies, detection systems, and staining platforms (e.g., Ventana, Dako, Leica).
- Optimize assay parameters, including epitope retrieval, antibody dilution, detection chemistry, and staining conditions.
- Establish scoring algorithms, cut-off strategies, and interpretation guidelines in collaboration with pathologists.
- Support biomarker strategies for patient selection and stratification in cli