Consultant - Immunogenecity

Role & responsibilities

As our Immunology SME, you will be expected to:

• Provide Strategic Guidance:

  Serve as the primary advisor to our computational and AI teams on all aspects of adaptive immunity and immunogenicity.

• Guide Model Development:

  Translate complex biological concepts (e.g., antigen processing, T/B cell activation, tolerance) into clear, actionable requirements for our data scientists.

• Validate Training Data:

  Advise on the curation, annotation, and validation of training datasets (e.g., T/B cell epitope libraries) to ensure biological relevance and data quality.

• Advise on Feature Engineering:

  Provide expert input on the key biological features (e.g., PTMs, aggregation factors, HLA binding promiscuity) that are critical for predicting immunogenicity.

• Critically Review Model Outputs:

  Act as the ultimate "sense check" for our model's predictions. You will be responsible for identifying when outputs contradict established immunological principles and for suggesting improvements.

• Conduct Immunogenicity Risk Assessments:

  Apply your expertise to review in silico data and provide strategic risk assessments for novel therapeutic protein candidates.

Preferred candidate profile

We are looking for a true expert with a deep and nuanced understanding of immunology.

Essential Domain Expertise:

• PhD

  in Immunology, Molecular Immunology, or a related field (postdoctoral experience highly preferred).

• Extensive experience

  (ideally 10+ years) in

  adaptive immunity

  , with deep knowledge of:
• T-cell and B-cell biology
• Antigen presentation (MHC Class I & II)
• Immunological tolerance (central and peripheral)
• TCR and BCR recognition

• Proven SME in Protein Immunogenicity

  , specifically for biologics (mAbs, bispecifics, ADCs, fusion proteins, etc.).

• Deep knowledge of Anti-Drug Antibody (ADA)

  development mechanisms and the factors that influence immunogenicity (e.g., aggregation, impurities, PTMs).

• Expertise in T-Cell & B-Cell Epitope Biology

  , including conformational vs. linear epitopes, HLA promiscuity, and population coverage.

Computational & Collaborative Skills:

• You are not a coder

  , but you must understand the concepts of AI/ML and computational biology.
• Familiarity with the principles of in silico epitope prediction tools (e.g., NetMHCpan) and their limitations.
• Experience (or strong understanding) of what is required to train and validate a model (e.g., training/test sets, validation metrics).

• Exceptional communication skills

  with a proven ability to collaborate effectively with data scientists and translate complex biology into computational language.