Scientist Manager, Bio-AI & ML Ops
Procter & Gamble is hiring a Scientist Manager, Bio-AI & ML Ops. Level rates it ; you can apply on Procter & Gamble.
Level's read on this job
- On Level's scale, this role is Builds AI ●●●●: the job is building AI systems.
- Tools and skills the posting cites include RAG, AI agents and MLOps.
- Level lists 872 other open roles at Procter & Gamble.
AI in this role
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 ProfessionalsHow we rate this
Scientist Manager, Bio-AI & ML Ops at Procter & Gamble rates 96 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Questions you could be asked
- How would you design a retrieval step so the model answers from real data instead of guessing?
- How do you decide when an AI agent can act on its own versus asking for approval first?
- How do you monitor a model once it's live, and how do you know it needs retraining?
- How would you decide a model or AI system is ready to ship?
- Tell me about a time a model underperformed in production. How did you find out, and what did you change?
Adapt your resume
- List these exact terms on your resume: RAG, AI agents, and ML Ops. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.
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