Data Science Manager

Job Description

Are you a (Senior) Data Science Manager and eager to consider a new opportunity ?

How about a job opportunity at a Data Analytics & Publishing company in Amsterdam or London?

General Information

Duration: 12 month contract or indefinite from the start

No. of working hours: 36 hours per week

Location: 1 / 2 days per week in Amsterdam or London

Contract type: FTE, Direct contract with client

Other: You need to be a resident of NL OR the UK at time of application

Available salary: Data Science manager: 80.000 – 131.000k in either Euro or GBP

Senior DS Manager: 100.000 – 167.000 in either Euro or GBP

We are seeking 2 experienced Managers Data Science to lead multidisciplinary team of up to 10 data scientists delivering AI-powered solutions for scientific and enterprise applications.

This position combines people leadership (60% of your time) with technical oversight (40% of your time). You will define priorities, guide execution, mentor team members, and ensure that robust data science methodologies are applied to solve complex customer and business challenges.

The team works across a diverse AI portfolio including machine learning, natural language processing, information retrieval, semantic technologies, knowledge representation, recommendation systems, predictive modelling, and large language model applications.

You will collaborate closely with product leadership, software engineering teams, subject matter experts, and business stakeholders to deliver scalable, measurable, production-grade AI solutions.

Responsibilities

Leadership & Team Development (60%)

Lead, mentor, and support a team of data scientists, enabling technical excellence and professional growth.

Define team objectives, priorities, and delivery plans aligned with organizational strategy.

Allocate resources across multiple initiatives while balancing short-term delivery and long-term innovation.

Foster a culture of collaboration, experimentation, responsible AI practices, customer focus, and continuous improvement.

Establish and maintain best practices for experimentation, model development, evaluation methodologies, and collaboration with engineering teams.

Data Science & AI Delivery (40%)

Guide the development and deployment of advanced AI solutions using machine learning, statistical modelling, natural language processing, neural networks, information retrieval, recommendation algorithms, semantic technologies, and generative AI.

Oversee systems supporting tasks such as:

document classification

information extraction

entity recognition

entity linking

semantic enrichment

ranking and retrieval

prediction and decision support

Drive initiatives that integrate structured and unstructured data from multiple scientific and technical sources.

Evaluate and implement modern AI approaches including embeddings, large language models, retrieval-augmented generation (RAG), prompt engineering, AI agents, and multi-agent architectures where they provide measurable value.

Partner with engineering teams to ensure solutions are scalable, maintainable, reliable, and production-ready.

Evaluation & Quality

Define evaluation frameworks for machine learning models, search systems, NLP pipelines, and AI-enabled product capabilities.

Establish metrics for retrieval quality, model performance, ranking effectiveness, data accuracy, user experience, and business outcomes.

Lead offline evaluation, A/B testing, annotation processes, error analysis, and human-in-the-loop validation activities.

Promote responsible AI principles including transparency, fairness, explainability, privacy, governance, and risk mitigation.

Encourage evidence-based decision making supported by clear communication of findings and recommendations.

Cross-Functional Collaboration

Work closely with product managers, engineers, domain specialists, information architects, data experts, and commercial stakeholders.

Translate business objectives into measurable AI initiatives and delivery roadmaps.

Present technical concepts, trade-offs, risks, and recommendations to both technical and executive audiences.

Contribute to the broader AI strategy and represent the data science function within cross-functional planning activities.

Required Qualifications

Master's degree or PhD in Computer Science, Data Science, Machine Learning, Statistics, Information Retrieval, Bioinformatics, Applied Mathematics, Artificial Intelligence, or a related discipline, or equivalent practical experience.

At least 3 years of experience leading or managing technical teams.

Experience reviewing technical work, mentoring data scientists, and improving engineering and data science practices.

At least 5 years of experience in data science:

applied machine learning, python, data science libraries, natural language processing, statistical modelling, information retrieval, or AI solution development.

Strong understanding of experimentation, supervised and unsupervised learning techniques, statistical analysis, model evaluation, experimentation, and generative AI concepts.

Experience working with large-scale structured and unstructured datasets.

Hands-on experience with large language models, embedding techniques, retrieval-augmented generation, AI evaluation methodologies, and human-assisted annotation workflows.

Familiarity with modern AI development ecosystems and frameworks such as deep learning libraries, experiment tracking platforms, orchestration frameworks, vector search technologies, or equivalent tools.

You can check other job opportunities in our website: Jobs – Magno IT.

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