Role
The Scientist II, Computational Biology in AI & Multimodal Target Discovery will advance target discovery and translational research by building rigorous computational methods that connect human disease evidence to target hypotheses, predict effects of genetic and pharmacologic interventions, and guide experimental design. The role partners closely with Immunology, Translational Science, Biologics and other functions to deliver reproducible analyses and reusable scientific frameworks for target nomination and downstream decision points.
Key Responsibilities
- Integrate and analyze multi-omics datasets (bulk and single-cell transcriptomics, spatial omics, proteomics, human genetics, functional genomics, clinical/translational data) to identify disease-relevant cell states, pathways, biomarkers, and targets.
- Develop AI agents to support scientific workflows (literature/knowledge retrieval, data quality assessment, analysis planning, code/tool execution, hypothesis generation, and result synthesis) with human review, traceability, access controls, and evaluation.
- Create transparent target-identification and prioritization frameworks combining genetic evidence, causal inference, knowledge graphs, tractability, safety, and translational relevance; communicate evidence strength and uncertainty.
- Build and apply virtual-cell and perturbation-response models to predict effects of genetic and pharmacologic perturbations; collaborate with lab scientists on prospective validation and model refinement.
- Apply computational structural biology approaches (structure prediction and assessment, variant/domain interpretation, interaction-site analysis, docking, molecular dynamics) to assess target biology and tractability.
- Develop reusable, maintainable computational frameworks and scalable workflows for ingestion, harmonization, analysis, model training, and reporting; establish standards for versioning, provenance, QC, reproducibility, and secure data handling.
- Track and evaluate emerging methods in multimodal foundation models, agentic AI, structural modeling, and computational biology and adopt approaches that demonstrate scientific or operational advantage.
- Communicate methods, findings, limitations, and recommendations through visualizations, technical documentation, presentations, and publications.
Required Qualifications
- Ph.D. in Computational Biology, Bioinformatics, Computer Science, or a related quantitative discipline, with 0–3 years of industry experience.
- Demonstrated depth in at least two areas: multi-omics target discovery, AI agent development, computational structural biology, or cellular perturbation modeling; ability and interest to work across others.
- Experience applying computational biology and ML/AI to drug-discovery or translational research in a pharmaceutical setting.
- Hands-on multi-omics analysis and biological interpretation; single-cell transcriptomics required; multimodal integration strongly preferred.
- Strong programming skills in Python, Java, C#, or R, and sound practices in software design, testing, version control, documentation, and reproducible analysis. Vibe coding under robust code review and management is encouraged.
- Working knowledge of modern ML/DL/AI methods for biological data, including model evaluation, prevention of data leakage, uncertainty assessment, and benchmark design.
- Ability to translate ambiguous scientific questions into testable computational hypotheses and explain methods and assumptions to multidisciplinary audiences.
- Strong collaboration skills and understanding of target discovery, validation, and translational research, particularly in oncology, hematology, or immunology.
Preferred Qualifications
- Experience with perturbational datasets and methods such as CRISPR screens, Perturb-seq, and transcriptomic drug-response resources; experience with multimodal or foundation models and counterfactual prediction.
- Experience building LLM-based or agentic systems (tool calling, retrieval-augmented generation, workflow orchestration, structured outputs, evaluation harnesses, human-in-the-loop controls).
- Experience with protein structure prediction, molecular visualization, sequence-to-structure analysis, docking, molecular dynamics, or protein-protein interaction modeling.
- Familiarity with cloud or high-performance computing, containers, workflow engines, relational or graph databases, and production-oriented model or data pipelines.
- Record of scientific publication, open-source contributions, patents, or delivery of computational methods that influenced experimental or portfolio decisions.
Location and Team Context
This position is located in the PharmaEssentia Innovation Research Center (PIRC), the company’s U.S. R&D center. The role is part of the Data Science, PIRC department and reports to the Director of AI in Drug Development.
