Senior Data Scientist

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As a Senior Data Scientist at Monks, you’ll help shape and deliver cutting-edge data solutions that power intelligent, personalised experiences for some of the world’s most influential brands. You’ll lead the design, development, and deployment of Machine Learning (ML) and Generative AI (GenAI) solutions that deliver measurable business impact, from predictive audience modelling to agentic automation systems, connecting data, intelligence, and action. You’ll work across the full data lifecycle, from ingestion and transformation to model orchestration and real-time activation, building the foundations that enable adaptive, insight-driven experiences at scale

You will partner with UX leads, data scientists and creative technologists, bridging the gap between data engineering, applied machine learning, and emerging agentic workflows. You’ll define and enforce standards that ensure that data, models, and automation pipelines are scalable, resilient, and fully governed at runtime. You’ll shape the technical architecture for AI-driven systems that can reason, act, and learn autonomously across complex digital environments, connecting real-time data, contextual intelligence, and orchestration layers to deliver faster, more adaptive, and continuously improving customer experiences.

You bring a solid foundation in modern cloud ecosystems (GCP, AWS, or Azure), data warehousing (BigQuery, Snowflake, etc.), and advanced ETL/ELT orchestration. Your deep understanding of machine learning and automation frameworks enables you to architect and operationalize predictive models, recommender systems, and agent-assisted optimization workflows.

This Netherlands-based role supports our Interactive team in delivering data-driven innovation for a major Dutch client. You’ll work in a fast-paced, collaborative environment at the forefront of AI, automation, and digital experience design, where your technical expertise helps connect data, creativity, and performance.

If you’re passionate about engineering intelligence from data and advancing autonomous, adaptive systems, we’d love to meet you.

• Lead the design, development, and deployment of advanced Machine Learning (ML) and Generative AI (GenAI) models on cloud platforms such as GCP and AWS, ensuring they are scalable, resilient, and compliant with enterprise standards.

• Architect and implement predictive frameworks, including propensity, churn, and recommendation models, translating analytical results into measurable business impact.
• Design and operationalise GenAI architectures using APIs (e.g., OpenAI, Gemini, Anthropic) and frameworks like LangChain or LlamaIndex, integrating them into production-grade workflows.
• Define data science standards and modelling pipelines, including feature engineering, data versioning, and reproducibility practices using tools such as dbt and Dataform.
• Oversee data integration and transformation workflows, automating ingestion and orchestration across APIs, cloud services, and real-time data pipelines.
• Establish MLOps best practices for continuous delivery, monitoring, and retraining of models in production environments.
• Ensure governance, observability, and documentation across all machine learning assets, adhering to internal standards for reproducibility, ethics, and security.
• Design and evaluate experimental frameworks (A/B, multivariate, or causal) to measure model impact, validate hypotheses, and translate insights into actionable optimization strategies.
• Inspire and guide data engineers, UX strategists, and creative technologists in translating data and AI into intelligent, human-centered digital experiences.
• Communicate complex analytical outcomes through clear visualisations and narratives, influencing technical and non-technical stakeholders alike.

• MSc in a quantitative discipline such as Data Science, Computer Science, Mathematics, Statistics, or a related field.
• 4+ years of professional experience applying data science in production environments, ideally across marketing, product, or customer-experience domains.
• Proven ability to design, develop, and deploy scalable machine learning and GenAI solutions on cloud platforms such as GCP or AWS.
• Experience working in cloud-native data ecosystems, including data warehousing (BigQuery, Snowflake, Redshift) and modelling frameworks such as dbt or Dataform.
• Deep understanding of statistical inference, machine learning algorithms, and experimental design, with the ability to translate analytical outcomes into actionable recommendations.
• Practical experience integrating APIs and automation frameworks (e.g., LangChain, Airflow, Cloud Functions) for data and AI orchestration.
• Familiarity with MLOps principles, including CI/CD pipelines, model versioning, and continuous monitoring in production.
• Demonstrated ability to communicate complex concepts clearly to both technical and non-technical stakeholders, influencing strategy and decision-making.
• Strong analytical, problem-solving, and collaborative leadership skills, with the ability to mentor others and drive cross-disciplinary innovation.
• Professional cloud certification (e.g. Google Cloud Professional Data Engineer or Machine Learning Engineer, AWS Certified Machine Learning - Specialty, or Azure Data Scientist Associate).
• Native-level Dutch and fluent in English, both written and spoken.

• Experience in consultancy or agency environments, leading end-to-end data science initiatives across multiple clients, industries, and stakeholder ecosystems.
• Applied experience with GenAI or LLM-based systems, such as retrieval-augmented generation (RAG), prompt engineering, or embedding-based personalisation.
• Familiarity with ML governance and Responsible AI principles, including model monitoring, bias mitigation, and explainability.
• Familiarity with marketing and behavioural ioral data, including analytics platforms and media performance data from digital advertising ecosystems.
• Understanding of digital marketing and advertising industry practices across client verticals such as retail, finance, and government.
• Contributions to the data or AI community (e.g., open-source projects, publications, conference talks) are a strong plus.

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