Manager, MCICC

Job Description

Main Responsibilities

We are seeking a highly skilled AIDD/CADD scientist with strong AIdriven drug evaluation and CADD capabilities to join our team and drive innovation in smallmolecule drug evaluation and development.
This role focuses on building integrating and applying AI and physicsinformed computational approaches to support decisionmaking.

Key responsibilities include:

• Design develop and deploy AIpowered systems for smallmolecule drug evaluation and development integrating data from chemistry biology and structural modeling.
• Apply and extend CADD methodologies (e.g. structurebased and ligandbased approaches) to support drug design and evaluation.
• Combine machine learning models with CADD workflows including virtual screening molecular property prediction binding/pose assessment and developability evaluation.
• Collaborate closely with medicinal chemists AI scientist biologists and project teams to translate scientific hypotheses and project needs into actionable computational strategies.
• Integrate target biology disease mechanisms and chemical design strategies to support rational decisionmaking.
• Rapidly prototype iterate and optimize models and workflows in a fastpaced R&D environment.
• Contribute to crossproject AI/CADD platforms that enable scalable reusable and decisionfocused drug evaluation capabilities.

Qualifications

• PhD or Masters degree with equivalent practical experience in CADDComputational Chemistry Computational Biology AI Machine Learning or a related technical field.
• Handson experience in AIbased smallmolecule drug evaluation or CADDrelated applications.
• Solid understanding of smallmolecule drug discovery workflows including hit identification lead optimization and decision checkpoints.
• Strong experience with machine learning frameworks such as PyTorch TensorFlow or JAX and scientific computing tools such as NumPy SciPy Pandas.
• Experience working collaboratively with chemistry and biology scientists with the ability to integrate domain knowledge into computational models.
• Strong problemsolving skills and the ability to learn quickly in a fastpaced environment.
• Excellent communication skills for crossdisciplinary collaboration.

• Practical expertise in CADD techniques such as:
  • Structurebased drug design (e.g. docking binding pose analysis)
  • Ligandbased modeling (QSAR similarity pharmacophore concepts)
  • Physicsinformed or hybrid ML/physics approaches
• Experience in AI models for drug discovery including graph neural networks generative models structure prediction or property prediction.
• Experience in disease biology and target research with demonstrated ability to link biological context to chemical design and evaluation strategies.
• Familiarity with real pharmaceutical R&D decision processes project milestones and tradeoffs (e.g. potency vs. ADMET vs. developability).
• Experience building tools or workflows that support project teams rather than standalone research prototypes.

Required Skills:

Preferred Skills:

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