20 Apr 2026

Senior Data Scientist

Monks

Amsterdam Area

20 Apr 2026

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Senior Data Scientist role at Monks in Amsterdam, focusing on designing and deploying ML and GenAI solutions for global brands. Requires 4+ years of experience, MSc in a quantitative field, and expertise in cloud platforms and data warehousing. Responsibilities include leading model development, establishing MLOps practices, and collaborating with cross-functional teams.

On-siteFull-timeSeniorMachine LearningGenerative AI

Senior Data Scientist

Apply

On-siteFull-timeSeniorMachine Learning

Salary

Not specified

Work Location

Amsterdam Area, NL

Work Model

On-site

Experience Required

4 years

Employment Type

Full-time

Experience Level

Senior

Core Qualifications

Technical (Must-have)

Machine LearningGenerative AIGCPAWSAzureBigQuerySnowflakeETL/ELT orchestrationMLOpsstatistical inference

Soft Skills

communicationproblem-solvingcollaborative leadershipmentoringinfluencing

Tools (Must-have)

OpenAIGeminiAnthropicLangChainLlamaIndexdbtDataformAirflowCloud Functions

Preferred Qualifications

Technical (Nice-to-have)

GenAILLM-based systemsretrieval-augmented generationprompt engineeringembedding-based personalisationML governanceResponsible AIbias mitigationexplainability

Tools (Nice-to-have)

analytics platformsmedia performance data

Key Responsibilities

•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.

Data ScienceMachine LearningGenerative AICloudGCPAWSMLOpsAdvertising ServicesSeniorAmsterdam

Key Responsibilities

•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.