Data Science Manager

Jobtailor; Amsterdam; On-site; EUR 140,000 - 190,000; Full time

Job summary

Jobtailor is hiring a Lead Data Science Leader to guide a team of data scientists in Amsterdam. You will set strategy, manage multiple projects, and drive production-ready AI solutions in collaboration with engineering and business stakeholders.

You will champion responsible AI, foster scientific rigor, and translate business needs into actionable data science initiatives across Life Sciences and Corporate Markets.

Qualifications

• Master’s or PhD in Computer Science, Data Science, ML, Statistics, or related field, or equivalent experience
• Significant experience in data science, ML, NLP, statistical modelling, or applied AI
• Experience leading technical teams directly
• Strong understanding of supervised/unsupervised learning, Gen AI, model evaluation and experimentation
• Experience with large structured and unstructured data
• Ability to manage multiple projects and stakeholders
• Experience with LLMs, RAG, embeddings, GenAI evaluation, or human-in-the-loop workflows
• Familiarity with production ML systems, MLOps, data pipelines, and model monitoring

Responsibilities

• Lead, coach, and develop a team of data scientists
• Set team strategy, priorities, and operating rhythm aligned with goals
• Plan, delegate, and manage resources across projects
• Foster scientific rigor, responsible AI, customer focus, and continuous improvement
• Define best practices for data science, experimentation, model evaluation, and production collaboration
• Lead ML methods across ML, NLP, neural networks, search, and knowledge graphs
• Oversee models and pipelines for classification, extraction, enrichment, prediction, and decision support
• Guide embeddings, LLMs, and GenAI evaluation
• Partner with engineering on scalable, production-ready solutions
• Define evaluation approaches for models, search systems, NLP pipelines, and AI features
• Promote responsible AI practices including fairness, explainability, privacy, and risk management
• Communicate evidence-based results and recommendations to stakeholders
• Collaborate with product managers, engineers, ontologists, and business leads
• Translate needs into data science opportunities and measurable outcomes
• Represent the team in cross-functional planning and strategy

Skills

Data Science Leadership

Team Coaching

Stakeholder Management

Communication

Collaboration

Continuous Improvement

Education

Master’s Degree

PhD

Tools

Databricks

PyTorch

Hugging Face

LangChain

LangGraph

Haystack

MLflow

Python

Job description

• Lead, coach, and develop a team of data scientists
• Set team strategy, priorities, and operating rhythm aligned with Corporate Markets and Life Sciences goals
• Plan, delegate, and manage team resources across multiple projects and product areas
• Foster scientific rigor, collaboration, responsible AI, customer focus, and continuous improvement
• Define and apply best practices for data science, experimentation, model evaluation, data quality, and production collaboration
• Lead data science methods across machine learning, statistical modelling, NLP, neural networks, search, recommendation, knowledge graphs, and generative AI
• Oversee models and pipelines for classification, entity recognition, entity linking, document understanding, ranking, extraction, enrichment, prediction, and decision support
• Support integration of structured and unstructured scientific data
• Guide embeddings, LLMs, RAG, prompt-based workflows, and GenAI evaluation
• Partner with engineering on robust, scalable, maintainable, production-ready solutions
• Define evaluation approaches for models, search systems, NLP pipelines, and AI-powered product features
• Guide offline evaluation, A/B testing, error analysis, annotation workflows, and human-in-the-loop evaluation
• Promote responsible AI practices including transparency, fairness, bias assessment, explainability, privacy, and risk management
• Communicate evidence-based results, technical findings, trade-offs, risks, and recommendations
• Collaborate with product managers, engineers, content specialists, ontology experts, biomedical informaticians, and commercial stakeholders
• Translate customer and business needs into data science opportunities, project plans, and measurable outcomes
• Represent the team in cross-functional planning and contribute to Life Sciences data science and AI strategy

Requirements

• Master’s, or PhD in Computer Science, Data Science, Machine Learning, Statistics, Bioinformatics, Cheminformatics, Information Retrieval, or a related field, or equivalent practical experience
• Significant experience in data science, machine learning, NLP, statistical modelling, information retrieval, or applied AI
• Experience managing or leading technical teams directly
• Strong understanding of supervised and unsupervised learning, Gen AI, statistical analysis, model evaluation, and experimentation
• Practical experience with Python and common data science, machine learning, or NLP frameworks
• Experience working with large, complex, structured and unstructured datasets
• Ability to manage multiple projects, prioritize work, and deliver through others
• Strong communication and stakeholder management skills
• Ability to coach data scientists, review technical work, and improve team practices
• Experience with LLMs, RAG pipelines, embeddings, GenAI evaluation, or human-in-the-loop annotation workflows
• Experience with Databricks, PyTorch, Hugging Face, LangChain, LangGraph, Haystack, MLflow, or similar
• Preferred: experience in life sciences, pharmaceuticals, chemistry, biomedical research, or clinical data
• Preferred: familiarity with ontologies, taxonomies, controlled vocabularies, and metadata standards
• Preferred: experience with NLP, entity extraction, entity linking, semantic enrichment, search, ranking, recommendation, or knowledge graph methods
• Preferred: exposure to production ML systems, MLOps, data pipelines, and model monitoring

Core Competencies

Demonstrates expertise in leading data science teams, applying machine learning and NLP techniques, and managing complex projects in alignment with corporate goals. Proficient in fostering collaboration, responsible AI practices, and translating business needs into actionable data science strategies.

Highest-signal resume keywords

• Data Science Leadership
• Machine Learning Expertise
• NLP Frameworks
• Project Management
• Responsible AI Practices

ATS Optimization Keywords

Hard Skills

• Machine Learning
• Statistical Modelling
• Natural Language Processing
• Data Evaluation
• Supervised Learning
• Unsupervised Learning
• Data Quality
• Model Evaluation
• Experimentation
• Information Retrieval

Soft Skills

• Team Coaching
• Stakeholder Management
• Communication
• Collaboration
• Continuous Improvement

Certifications & Qualifications

• Master’s Degree
• PhD

Industry Keywords

• Life Sciences
• Pharmaceuticals
• Biomedical Research
• Clinical Data
• Ontologies
• Taxonomies
• Controlled Vocabularies
• Metadata Standards

Tools & Technologies

• Python
• Databricks
• PyTorch
• Hugging Face
• LangChain
• LangGraph
• Haystack
• MLflow