AI Product Owner (ML & GenAI) for Operations

Danone; Amsterdam; On-site; EUR 90,000 - 140,000; Full time

Benefits offered by this job

Yearly bonus

Pension plan

30 days annual leave

Health care allowance

Gym discount

Learning & development

Hybrid work environment

Job summary

Danone is seeking an AI Product Owner (ML & GenAI) for Operations to own AI products end to end, across factories, functions, and partners. You will be hands-on with data, build models, and drive value from ideation to deployment.

Ideal candidates have ML and GenAI experience, cloud platform knowledge (Azure/Databricks), and strong cross-functional collaboration skills. Fluent English is required; a hybrid Netherlands setup is offered.

Qualifications

• Own AI products for Operations from ideation to deployment.
• Translate business problems into use cases, data needs, and model choices.
• Lead cross-functional teams including IT, OT, data, and external partners.

Responsibilities

• Act as Product Owner for AI projects and products in Operations: vision, roadmap, backlog, and delivery.
• Build and evaluate models and prototypes, not just coordinate others.
• Define acceptance criteria and go-live standards; ensure models are explainable and robust.

Skills

Product ownership

ML foundation

Hands-on with data

GenAI literacy

Python

Cloud platforms

LangChain

Change management

Value tracking

Communication

Cross-functional collaboration

Fluent English

Education

Bachelor’s or Master’s in CS/Engineering/Data Science/Related field

Tools

Azure

Databricks

LangChain

Python

Java/C#

Job description

Danone is looking for an AI Product Owner (ML & GenAI) for Operations to own AI products for Operations end to end.

About the job

Mission of the role:

Own AI products for Operations — end to end.

• Act as Product Owner for AI projects and products in Operations: vision, roadmap, backlog, and delivery with factories, functions, zones, IT, OT, and external partners.
• Understand the mechanics behind the product (data, models, architecture, workflow) — and the process around it.
• Be hands‑on: work with data, build and evaluate models, and prototype solutions, not only coordinate others.
• Take use cases from ideation to deployment: change management, user coaching, and value tracking against the business case.
• Deliver PoCs, scale what works, kill what does not, and build in-house ML/GenAI capability.
• Define and run sustainable AI development and management practices in line with Responsible AI.

Product ownership

• Own the product roadmap for assigned AI products (ML platforms and GenAI solutions) and keep it aligned with Operations priorities and the business case.
• Translate business problems into use cases, data needs, model choices, and a sequenced backlog.
• Run the product with cross-functional teams: process / IWS, OT, data, IT, factories, and partners. Decide what is in, what is out, and when it is good enough to go live.
• Set acceptance criteria, go-live criteria, and model-evaluation standards. Challenge black-box delivery until mechanics and quality are understood.

Hands-on ML / data / GenAI

• Work with industrial data: tags, historians, time series, data gaps, cleansing, feature engineering.
• Build, train, evaluate, and iterate models (anomaly detection, predictive / remaining-useful-life style use cases, classical ML, and GenAI).
• Ensure a consistent, state-of-the-art architecture across products; experiment with new ML/GenAI techniques and bring them into the stack (Azure / Databricks and related tools).
• Deliver in-house solutions and integrations; test, troubleshoot, and debug until they work in a plant context.

Users, change, value

• Work with users from ideation to deployment: workshops, shadowing on the line, demos, training, hypercare.
• Drive change management so alerts and tools are used — not only installed. Coach key users and application owners.
• Track value (e.g. unplanned downtime, PR, quality, cost) versus the business case; feed results back into roadmap and model priorities.
• Document learnings and best practices so successful products can be scaled to further sites/lines.

Lab / CoE

• Animate the centre of excellence and AI self-service where it helps adoption.
• Mentor others and grow in-house capability so Operations is not dependent on a single expert or vendor.

Indicative KPIs

• Roadmap delivery: committed site/line onboardings and product increments on time.
• Model / product quality: agreed evaluation criteria met before go-live; alert precision/usefulness in operation.
• Adoption: share of modelled assets / target users actually using the product (actioned alerts, active companions).
• Value: realized vs business-case impact, with a clear measurement method.
• Lab throughput: PoCs taken to a working product or explicitly stopped, with documented learnings (indicative ~4–5 prototypes/year, 2–3 months each).

Skills needed for the role

• Product ownership in a technical domain: roadmap, backlog, prioritization, stakeholder management, and the ability to say no. Comfortable as the single owner of an AI product, not as a pure project coordinator.
• Solid ML foundation: supervised/unsupervised learning, time-series and anomaly detection, feature engineering, model evaluation, and the judgement to choose the right model for the loss — not the most fashionable one.
• Hands‑on with data: exploratory analysis, industrial/OT data (sensors, tags, historians), data quality, and working with process engineers on loss trees and critical equipment.
• GenAI literacy: foundation models, orchestration (e.g. LangChain / similar), and when GenAI is the right tool vs classical ML.
• Proficiency in at least one language used for data/ML work (Python preferred; Java/C# a plus). Comfortable writing code, not only slides.
• Experience with cloud data/ML platforms (preferably Azure, Databricks).
• Change management and user empathy: can work with operators, maintainers, supply chain, procurement and leadership from idea through go-live.
• Value tracking: can turn a use case into a measurable business case and keep score after deployment.
• Strong collaboration and communication: explain models and trade‑offs to non‑technical stakeholders; work with IT, Data, OT, and partners.
• Innovative but pragmatic: experiment fast, stay current, ship what plants will use.
• Fluent English.

About you

• Bachelor’s or Master’s in Computer Science, Engineering, Data Science, Industrial Engineering, or a related field. PhD is a plus; equivalent hands‑on experience equally valued.
• Professional experience in IT/Data/AI, with a mix of machine learning delivery and product ownership (or a technical lead who has owned a product end to end).
• Demonstrated experience taking an ML or AI solution from idea to users in a live operational environment — including messy data, model iteration, and adoption — not only a notebook or a vendor demo.
• Experience with industrial or operations context is a strong plus (manufacturing, maintenance, process, OT/IT).
• Experience working with cross‑functional teams, plants/sites, and external partners.
• Experience with cloud platforms (Azure, AWS, or GCP) and with programming/frameworks used in ML and backend development.
• Project/product delivery track record: multiple workstreams, clear priorities, on‑time delivery.

About Danone

At Danone we are committed to our mission “bringing health, through food to as many people as possible”. We are working together to make sure that our brands, like Hipro, Nutricia, Alpro, Activia, Nutrilon and Evian, create real benefits for people, communities and the planet. You will be part of one of the largest Certified B Corps™ in the world. Being B Corp means to be part of a select movement of companies verified to be meeting the highest standards of social and environmental criteria and using business as a force for good.

We offer:

All Danoners receive a complete package of benefits. This includes a competitive salary and yearly bonus, a premium free pension, 30 days of annual leave (with the opportunity to purchase more) and you will receive a monthly health care allowance. In addition, you can get discount on your gym membership, and we offer initiatives focused on nutrition, physical, and mental health. We provide continuous learning opportunities through workshops, online courses and training sessions.

In the Netherlands we also offer a hybrid working environment which enables each team to meet our people's desire for flexibility. You can work from one of our People Hubs in Hoofddorp, Utrecht and Rijswijk or you can work from home.