Scientist Manager, Bio-AI & ML Ops

Location: Singapore TC-Biopolis

Company

Procter & Gambl produces globally recognized consumer brands and operates in approximately 70 countries.

What you will do

• Design multi-agent scientific workflows for evidence retrieval, hypothesis generation and evaluation, mechanistic reasoning, experiment planning, and decision support.
• Build RAG and Graph-RAG solutions using biological databases, scientific literature, patents, supplier data, formulation knowledge, APIs, simulation tools, OMICS pipelines, dashboards, and scientific models.
• Partner with scientists, bioinformaticians, and data engineers to translate scientific workflows into agent-ready tasks and tools.
• Establish evaluation frameworks covering predictive performance, evidence quality, provenance, hallucination risk, reproducibility, and human review.
• Implement production controls for model, agent, prompt, and workflow versioning, observability, tracing, CI/CD, access control, monitoring, and auditability.
• Convert pilots into reusable platform capabilities and improve models through experimental outcomes and scientist feedback.

Requirements

• Bachelor’s, Master’s, or PhD degree in Data Science, Bioinformatics, Computational Biology, Biostatistics, or a related field.
• Strong Python engineering skills and hands-on experience delivering AI, machine-learning, or data solutions.
• Experience developing and deploying predictive models, LLM applications, retrieval systems, or agentic workflows.
• Experience with tool calling, structured outputs, workflow orchestration, and agent state or memory.
• Experience designing evaluation approaches for machine-learning, LLM, or agentic AI systems.
• Familiarity with APIs, cloud deployment, containers, CI/CD, monitoring, ML Ops, or LLM Ops.

Nice to have

• Experience with RAG, Graph-RAG, vector databases, tool-use architectures, and agent orchestration.
• Experience with biological, chemical, OMICS, assay, formulation, performance, or consumer data.
• Knowledge of scientific knowledge graphs, ontologies, pathway databases, literature and patent mining, or ELN/LIMS integration.
• Experience with human-in-the-loop and traceable decision systems.
• Exposure to biological foundation models, multimodal scientific AI, active learning, or AI Co-Scientist systems.

Culture & Benefits

• Work as part of a growing team of functional and technical experts at the Singapore Innovation Center at Biopolis.
• Report to a locally based R&D Director and contribute to innovation projects across the region.
• Join an organization focused on inclusion, equal employment opportunity, and developing consumer brands and products.