Top Machine Learning Jobs in the Netherlands

Develop Elastic observability and security solutions for application and infrastructure teams. Build Kibana user interfaces and REST APIs, analyze IT operations data, create Elastic rules and SIEM detections, implement Elastic Security modules, and automate zero-touch deployments with Ansible. Collaborate with customers, UI teams, product management, and IT Operations while improving infrastructure automation and service quality. The role is hybrid in the Netherlands and requires Dutch nationality, a Dutch passport, and Dutch-language proficiency.

Develop Elastic-based security and observability solutions for applications and infrastructure. Build Kibana user interfaces and REST APIs, analyze IT operations data, create Elastic and SIEM detection rules, implement Elastic Security modules, and automate zero-touch deployments with Ansible. Collaborate with customers, UI teams, product management, and IT Operations to improve monitoring, security, automation, and operational efficiency.

Consults with banks, insurers, and asset managers to build and validate statistical, predictive, and generative AI models for financial risk, AML, fraud, sanctions, and regulatory compliance. Analyzes complex financial datasets, supports AI model governance, applies machine learning to transaction monitoring, and communicates findings directly to clients. The role also involves project ownership, regulatory research, internal knowledge sharing, and identifying improvements to clients’ financial technology practices.

Collaborate with data scientists, engineers, and consultants on client projects. Apply statistical modeling, optimization, machine learning, Generative AI, and large language models to solve business challenges. Extract insights from data, present analytical findings, support client workshops, and translate technical concepts into actionable recommendations.

Lead the development of AI products and production-grade machine learning solutions for internal and client projects. Make cloud architecture and design decisions, scale data science models into production, mentor engineers, deliver training, and represent the organization through workshops, conferences, blogs, and events. The role also contributes to team growth, consulting engagements, and cross-functional collaboration across international data teams.

Collaborate with data scientists, engineers, and consultants on client projects. Apply statistical modeling, optimization, machine learning, Generative AI, and large language models to solve business problems. Extract actionable insights, develop analytical solutions, present results, and support client workshops in multidisciplinary teams.

Lead the growth of Sia’s machine learning engineering team, develop AI products and client solutions, make cloud architecture decisions, and build production-grade ML systems. Guide data scientists in deploying and optimizing models, mentor engineers, deliver training, support client workshops, and represent the company through conferences, blogs, and media. The role requires strong software engineering, cloud, big data, DevOps, communication, and consulting capabilities.

Leads client engagements involving advanced analytics, predictive and explanatory modeling, econometrics, optimization, forecasting, Generative AI, and LLMs. Advises senior stakeholders, guides multidisciplinary project teams, mentors colleagues, supports business development, and contributes to internal training, publications, thought leadership, and the development of data science and AI offerings.

Senior data science consultant developing machine learning, statistical, optimization, simulation, forecasting, and big data models for clients. Responsibilities include managing end-to-end projects, client relationships, consulting proposals, business development, publications, training, and supervising junior consultants. The role requires presenting insights, ensuring deliverable quality, mentoring team members, and contributing to the firm's data science offerings.

Lead client engagements involving advanced analytics, predictive modeling, econometrics, optimization, forecasting, Generative AI, and large language models. Manage stakeholders, guide multidisciplinary teams, mentor colleagues, provide strategic recommendations, contribute to thought leadership and internal offerings, and support business development through proposals and client relationship development.

Lead data science consulting engagements involving machine learning, statistics, operational research, optimization, simulation, forecasting, and big data. Manage projects end to end, client relationships, reporting, commercial proposals, and new opportunities. Contribute to thought leadership, training, publications, and firm offerings while supervising and developing junior consultants. Requires strong quantitative expertise, Python programming, consulting capability, and professional Dutch and English.

Leads machine learning engineering initiatives, develops production-grade AI solutions, makes cloud architecture decisions, and helps scale data science models into production. The role includes leading internal teams, advising clients, mentoring engineers, delivering training, organizing events, and representing the company through articles, conferences, and communications. It requires strong programming, cloud, DevOps, database, big data, and machine learning expertise, along with English and Dutch fluency and willingness to travel.

Collaborate with data scientists, engineers, and consultants on client projects. Apply advanced analytics, statistical modeling, optimization, machine learning, Generative AI, and large language models to solve business problems. Extract actionable insights, communicate analytical findings, and support client workshops. The role requires strong programming, technical communication, and the ability to translate complex analyses into practical recommendations.

Lead design and development of scalable front-end applications using React and TypeScript for an AI/ML-powered cloud-native SaaS platform. Collaborate with product, UX, backend, and ML teams to integrate APIs and real-time services, drive front-end architecture, optimize performance and accessibility, and contribute to testing and continuous improvement.

Lead redesign of B2B underwriting models and credit frameworks, improve data analysis and processes, support multi-country product expansion, partner with Data Science on ML models, enhance reporting and KPIs, and collaborate with cross-functional teams to steer B2B risk strategy.

Conduct applied AI and machine learning research for an AI sales development platform. Responsibilities include designing privacy-preserving models, preprocessing data, training models, running experiments, integrating prototypes into production, interpreting research papers, publishing internal reports, and ensuring GDPR-compliant implementations. The role requires collaboration with engineering and stakeholders in a fast-paced startup environment.

Conduct applied AI/ML research to help an AI sales platform rapidly adapt to individual customer environments using sparse interaction data. Responsibilities include designing learning and feedback strategies, building and evaluating prototypes, integrating models into production, researching academic literature, presenting findings, and ensuring GDPR-compliant implementations. The role involves close collaboration with engineering and offers a potential path to lead the company’s applied research function.

Lead client engagements delivering advanced predictive and explanatory models and Generative AI/LLM solutions. Manage stakeholders, mentor multidisciplinary teams, drive strategy-to-execution delivery, support business development, and contribute to internal training and thought leadership.

Lead client engagements delivering advanced predictive models and Generative AI/LLM solutions. Manage stakeholders and multidisciplinary teams, mentor colleagues, contribute to business development, and represent the firm in thought leadership and internal capability building.

Lead and deliver end-to-end data science and optimization projects: develop predictive and explanatory models, design optimization/simulation solutions, process large/unstructured data, manage client relationships, supervise consultants, contribute to business development and thought leadership.

Lead and grow the Data team, design cloud ML architectures, build production-grade ML/AI solutions, guide data scientists to productionize models, participate in client projects, train and mentor team members, and represent Sia externally through talks and publications.

Work with multidisciplinary teams to develop statistical models, optimization and machine learning solutions, and apply Generative AI/LLMs. Extract insights, present results, and facilitate client workshops to solve business problems.

Lead and grow the Data team to build production ML/AI products and client solutions: design cloud architectures, implement production-grade ML pipelines, scale models to production, mentor data scientists, run trainings and client workshops, and produce thought leadership.

Lead delivery of complex predictive and explanatory models (ML, statistics, econometrics), optimization and simulation, and big‑data solutions. Manage end-to-end projects, build client relationships, develop proposals and thought leadership, and mentor junior consultants.

Work in multidisciplinary teams to apply advanced analytics, machine learning, optimization, and Generative AI to solve client problems. Develop models, implement solutions, present results, and support client workshops while collaborating with data engineers and consultants.