Associate Scientific Manager - Structural Biologist (PhD/Masters)

Key Responsibilities

1. Scientific Program Coordination (Computational & AI-Focused)

• Manage multi-disciplinary discovery initiatives that span protein engineering, target assessment, sequence-level modeling, generative molecule design, and multi-omics interpretation.
• Assist in defining project scopes, scientific milestones, and computational deliverables.
• Track progress across ML modeling, bioinformatics analyses, structural modeling cycles, and insight-generation workflows.
• Ensure scientific alignment across computational biology, cheminformatics, ML engineering, and platformdevelopment teams.

2. Protein Engineering & Structural Biology Support

• Coordinate computational strategies related to:
• protein sequence modeling and mutational scanning
• structure prediction (AlphaFold, ESMFold, Rosetta-based workflows)
• binding interface characterization and proteinligand or proteinprotein interactions
• stability/solubility prediction, developability assessments, and variant prioritization
• Review structural models, annotated residue-level insights, and design outputs generated by ML/AIplatforms.
• Integrate sequence- and structure-based computational results into program-level decision-making.

3. Multi-Omics & Bioinformatics Workflow Management

• Collaborate with computational biologists to design and support analyses involving transcriptomics,proteomics, single-cell data, and genome-wide datasets.
• Aid in selection and interpretation of:
• differential expression analyses
• pathway enrichment and network biology modeling
• biomarker discovery and target prioritization from omics datasets
• Organize pipelines for integrating omics-derived insights with AI predictions, structural features, and medicinal chemistry landscapes.

4. Data Integration & ML Problem Formulation

• Translate biological/scientific questions into machine learning tasks such as:
• protein fitness prediction
• generative sequence design
• ligandtarget affinity prediction
• ADMET or MoA modeling
• large-scale virtual screening
• Collaborate with ML engineers on dataset preparation, feature construction, labeling strategies, and model evaluation.
• Review outputs from predictive models and convert them into actionable insights for discovery teams.

5. Computational Workflow Development & Documentation

• Maintain structured, reproducible workflows across structural modeling, omics analysis, genetic variant scoring, and ML-driven design loops.
• Evaluate and help standardize emerging computational tools, databases, and algorithms relevant to protein science, bioinformatics, and ML.
• Develop and maintain internal documentation, pathway diagrams, decision frameworks, and best-practice guidelines.

6. Scientific Communication & Cross-Functional Liaison

• Prepare clear, data-rich scientific briefs, platform feature requests, white papers, and internal strategy documents.
• Present computational insights to scientific leadership, domain experts, and cross-functional stakeholders.
• Support manuscripts, conference presentations, grant applications, and external scientific communication.

Required Qualifications

Educational Background

• Masters/PhD in Computational Biology, Bioinformatics, Protein Engineering, Structural Biology, Systems

Biology, Computational Chemistry, or a related discipline.

• 2–5 years experience in dry-lab drug discovery, computational R&D, or AI/ML-driven biological research,

or postdoctoral research in a relevant computational/biological field.

• Strong knowledge in at least two of the following areas and familiarity with the others:
• Protein engineering (sequence analysis, mutational scanning, stability modeling)
• Structural biology (structure prediction, docking, molecular interactions)
• Multi-omics analytics (RNA-seq, proteomics, scRNA-seq, integrative systems biology)
• Bioinformatics pipelines (annotation, variant analysis, network biology, functional genomics)
• ML-driven molecular/property prediction
• Ability to interpret sequence/structure/omics outputs and contextualize them within drug discoveryworkflows.
• Excellent scientific communication and project coordination abilities.