Scientist Manager, Bio-AI & ML Ops
Sign up free to see how well your resume matches this role.
What you'll 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 across approved internal and external sources, including biological databases, scientific literature, patents, ChEMBL, supplier data, and formulation knowledge.
- Enable agents to use internal databases, 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.
- Evaluate and apply emerging methods in agentic AI, AI Scientist systems, biological foundation models, and AI Co-Scientist architectures.
- Establish evaluation frameworks covering predictive performance, task success, evidence quality, provenance, hallucination risk, reasoning quality, reproducibility, and human-review outcomes.
- Implement production controls for model, agent, prompt, and workflow versioning, observability, tracing, CI/CD, access control, monitoring, and auditability.
- Convert pilots into reliable, reusable platform capabilities and scale validated workflows across programs.
- Build closed learning loops using experimental outcomes and scientist feedback to improve models, agents, and predictions.
What they're looking for
- Bachelors, Masters or PhD degree in Data Science, Bioinformatics, Computational Biology, Biostatistics, or a related field
- Strong Python engineering 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, and 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.
- Familiarity with 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.
Summarised by NextRaise from the employer’s description, which follows in full below.
Full description from employer
Job Location
SINGAPORE TC-BIOPOLISJob Description
Overview of Role
You will have the opportunity to lead the design, development, evaluation, deployment, and scaling of predictive Bio-AI and integrated Consumer data models and multi-agent workflows. You will own transforming trusted data, scientific knowledge, and domain tools into governed AI solutions that predict outcomes, generate and evaluate hypotheses, guide experimentation, and accelerate cross-OU innovation and decisions.
You will be responsible for predictive Bio-AI and integrated Consumer data models, agentic workflows, evaluation, deployment, monitoring, ML Ops, LLM Ops, and Continuous Integration and Delivery (CI/CD). If this sounds exciting to you, APPLY NOW!
Your team
Located at the P&G Singapore Innovation Center at Biopolis, you will report to a R&D Director based locally and will be part of growing team of functional and technical experts in leading projects in the region.
Key Responsibilities
1. Build Predictive and Agentic AI Solutions
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 across approved internal and external sources, including biological databases, scientific literature, patents, ChEMBL, supplier data, and formulation knowledge.
Enable agents to use internal databases, 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.
Evaluate and apply emerging methods in agentic AI, AI Scientist systems, biological foundation models, and AI Co-Scientist architectures.
2. Deploy, Validate, and Scale AI Capabilities
Establish evaluation frameworks covering predictive performance, task success, evidence quality, provenance, hallucination risk, reasoning quality, reproducibility, and human-review outcomes.
Implement production controls for model, agent, prompt, and workflow versioning, observability, tracing, CI/CD, access control, monitoring, and auditability.
Convert pilots into reliable, reusable platform capabilities and scale validated workflows across programs.
Build closed learning loops using experimental outcomes and scientist feedback to improve models, agents, and predictions.
Job Qualifications
Bachelors, Masters or PhD degree in Data Science, Bioinformatics, Computational Biology, Biostatistics, or a related field
Strong Python engineering 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, and ML Ops or LLM Ops.
Preferred Qualifications
Experience with RAG, Graph-RAG, vector databases, tool-use architectures, and agent orchestration.
Experience with biological, chemical, OMICS, assay, formulation, performance, or consumer data.
Familiarity with 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.
About us
We produce globally recognized brands and we grow the best business leaders in the industry. With a portfolio of trusted brands as diverse as ours, it is paramount our leaders are able to lead with courage the vast array of brands, categories and functions. We serve consumers around the world with one of the strongest portfolios of trusted, quality, leadership brands, including Always®, Ariel®, Gillette®, Head & Shoulders®, Herbal Essences®, Oral-B®, Pampers®, Pantene®, Tampax® and more. Our community includes operations in approximately 70 countries worldwide.
Visit http://www.pg.com to know more.
Our consumers are diverse and our talents - internally - mirror this diversity to best serve it. That is why we’re committed to building a winning culture based on Inclusion and our ideal candidate is passionate about the same principle: you will join our daily effort of being “in touch” so we craft brands and products to improve the lives of the world’s consumers now and in the future. We want you to inspire us with your unrivaled ideas.
We are committed to providing equal opportunities in employment. We do not discriminate against individuals on the basis of race, color, gender, age, national origin, religion, sexual orientation, gender identity or expression, marital status, citizenship, disability, veteran status, HIV/AIDS status, or any other legally protected factor.
Job Schedule
Full timeJob Number
R000159377Job Segmentation
Experienced ProfessionalsCompany
Company facts come from this company's own listings. We only show what the postings themselves carry.