Top Machine Learning Jobs in the Netherlands

Leads an international data science and AI team of up to eight members focused on commercial analytics and AI accelerators. Owns demand intake, prioritization, product strategy, model and data product delivery, and enterprise-scale ML/AI platform development. Partners with commercial, marketing, finance, and brand leaders to translate advanced analytics into business recommendations, revenue growth, and resource optimization. Requires executive communication, team-building, product management, and hands-on expertise in ML, LLMs, agentic workflows, cloud deployment, and pharma or healthcare analytics.

Lead the AI and innovation direction for Operations, identify and prioritize high-value use cases, and build a central Ops innovation model. Manage a small Forward Deployed AI team, partner with domains and engineering to deliver scalable solutions, track delivered value, and drive adoption and upskilling across the function.

Establish and scale FareHarbor’s revenue management and dynamic pricing practice. Analyze booking patterns, seasonality, channels, market conditions, and customer performance to improve revenue and yield. Develop onboarding standards, playbooks, training, tools, escalation paths, and implementation processes. Advise strategic customers while partnering with Product, Commercial, Data, Machine Learning, Implementation, and Customer Success teams to translate insights into product opportunities and measurable outcomes.

Leads Pfizer’s enterprise applied AI strategy, architecture, governance, operating model, and transformation efforts. Drives portfolio-level technical decisions, reusable capabilities, standards, and measurable business outcomes. Provides hands-on architecture and code review using modern AI and agent frameworks while leading engineers, contractors, and vendors. Oversees quality, security, performance, spending, and technical direction, mentors technical leaders, and evaluates emerging technologies and industry trends.

As a Senior AI Research Engineer, you will drive Adyen's AI strategy, build production-grade applications, optimize AI interactions in finance, mentor teams, and engage with the AI community.

Lead and develop globally distributed Data and AI teams to deliver production Data/AI solutions. Drive talent development, operational excellence, stakeholder alignment, and responsible AI practices while partnering with Product and Engineering to set priorities, roadmaps, and delivery metrics.

The Engineering Manager leads the Optimize Interfaces team, driving architecture strategy, mentoring engineers, collaborating across teams, and fostering an inclusive culture.

Lead architecture and technical strategy for merchant fraud prevention across multiple teams. Design high-throughput, low-latency distributed systems, integrate ML/LLM models, evolve data and inference pipelines, align product and compliance goals, standardize practices, and mentor senior engineers.

Lead global design, governance, and deployment of quantitative financial crime detection models and risk-rating engines. Translate regulatory requirements into algorithmic controls, calibrate monitoring and screening thresholds, validate models, and partner with Product, Engineering, and investigations teams to minimize false positives while maintaining detection performance. Represent model logic and tuning to regulators and banking partners.

Lead product vision and roadmap for agentic AI solutions, own the backlog, translate business needs into user stories, partner with data scientists and engineers, manage Agile delivery cycles, ensure responsible AI practices, and monitor model performance and continuous improvement.

Architect and build the AI layer for Neo4j’s Hume Maestro platform. Responsibilities include setting the AI technical roadmap, contributing hands-on to production code, designing scalable AI architectures, establishing model evaluation and deployment practices, integrating AI with front-end and platform systems, and collaborating with Product and Science stakeholders. The role may eventually evolve into leading and mentoring a dedicated AI engineering team.

Lead and scale a regional Market & Customer Intelligence program across EurAsia, turning market and customer insights into actionable, AI-enabled analytics and decision-ready narratives. Own rollout roadmaps, governance, intelligence cycles, stakeholder enablement, KPI tracking, and cross-functional adoption to embed the capability as a trusted enterprise asset.

The AI Engineer will build data pipelines and improve ML systems for reliability in production. Responsibilities include schema design, data infrastructure, and model evaluation, ensuring high-quality outputs and integration with product workflows.

Thesis internship in Forecasting Analytics: analyze demand-forecast data, test statistical and machine-learning forecasting methods, support projects, identify accuracy improvements, and present findings to improve forecast processes for part of the Mars Wrigley portfolio.

As AI Strategy Lead, you'll manage key clients and develop AI strategies for tech scale-ups, leveraging your consulting experience and network.

Lead the estimating product roadmap for an AI-powered construction SaaS: prioritize and ship MVPs, run rapid experiments, conduct user discovery with estimators, define success metrics, and iterate using data and user feedback.

Own full-cycle recruiting for technical roles, partner with hiring managers to define roles, source and close candidates, build scalable systems and pipelines, optimize hiring with data, and support a global, remote people team while acting as a culture ambassador.

As a Data Analyst, you'll advise clients on data management strategies, implement solutions, and enhance data governance and quality. You'll also provide insights for business decisions based on analytical data evaluations.

Lead and grow a backend engineering team building scalable, cloud-agnostic infrastructure and services for Databricks. Hire and develop engineers, ensure engineering excellence (architecture reviews, testing, monitoring), drive roadmaps with product leadership, and coordinate cross-team execution to deliver reliable distributed systems and ML infrastructure.

Join Snap Inc. as a thesis student exploring efficient on-device ML for AR applications, focusing on event-driven processors. Conduct research, develop models, and produce a thesis report over 8-12 months.

Lead strategy and delivery for knowledge management and AI in an AI-assisted research workspace. Own product vision, roadmap, and squad delivery for project spaces, personalization, and a researchers persistent memory. Run customer discovery, partner with retrieval and storage teams, work with embedded data scientists on evaluation, and turn evidence into prioritized product improvements toward an AI-maintained knowledge base.

Develop and lead the AI strategy for vulnerability and exposure management. Build AI-enabled solutions, automation, integrations, predictive analytics, and risk-based prioritization across vulnerability, asset, threat, and configuration data. Support CTEM operations, improve remediation and patching outcomes, and influence security, infrastructure, engineering, and technology stakeholders to adopt transformed workflows and best practices.

Work with the Data & Analytics team to solve client data science and analytics problems. Apply theoretical machine-learning and statistical knowledge in practice, collaborate with Data Scientists, Engineers, and Analysts, take on project tasks, and contribute to knowledge sharing and visualization deliverables.

The Data Scientist will design algorithms for insights, structure data, collaborate with machine learning engineers, and analyze datasets to improve workflow automation and product value.

Develop and maintain statistical forecasts across Nutreco's global portfolio using advanced analytics and forecasting models. Improve forecast accuracy in Infor Demand Forecasting, resolve exceptions, refine segmentation and models, ensure data integrity, track KPIs (Forecast Accuracy, Bias, FVA), identify demand drivers and external data, drive continuous improvement, and communicate actionable insights to stakeholders.