Manager Data Science

TL;DR

Manager Data Science (AI/ML, NLP, LLMs): Lead a team of data scientists delivering production-ready data science solutions for Life Sciences customers across machine learning, NLP, search, recommendation, knowledge graphs, and generative AI. Focus on building and evaluating scalable models and pipelines (including LLM-based systems, RAG, and GenAI evaluation) while ensuring responsible AI practices and measurable business impact.

Location: Amsterdam / London

Salary: €79,000 - €131,500

Company

hirify.global provides trusted content, data, and analytics to support researchers, clinicians, and life sciences professionals.

What you will do

• Lead, coach, and develop a team of data scientists; set strategy, priorities, and delivery rhythm for Corporate Markets Life Sciences.
• Oversee data science delivery across ML, statistical modelling, NLP, neural networks, search, recommendation, knowledge graphs, and generative AI.
• Develop and improve models and pipelines for classification, entity recognition/linking, document understanding, ranking, extraction, enrichment, prediction, and decision support.
• Guide integration of structured and unstructured scientific data (e.g., chemical entities, drugs, genes, diseases, clinical trials, safety data, publications, patents, metadata, ontologies).
• Define evaluation and experimentation approaches (offline evaluation, A/B testing, error analysis, human-in-the-loop) and ensure evidence-based decisions.
• Partner with product, engineering, content, and domain stakeholders to deliver scalable, measurable, production-ready solutions and communicate trade-offs and risks.

Requirements

• Master’s or PhD in Computer Science, Data Science, Machine Learning, Statistics, Bioinformatics, Cheminformatics, Information Retrieval, or related field, or equivalent practical experience.
• At least 5 years of experience in data science, machine learning, NLP, statistical modelling, information retrieval, or applied AI.
• Experience managing or leading technical teams directly.
• Strong understanding of data science methods, including supervised/unsupervised learning, GenAI, model evaluation, and experimentation.
• Practical experience with Python and common data science/ML/NLP frameworks; experience with large structured and unstructured datasets.
• Experience with LLMs, RAG pipelines, embeddings, GenAI evaluation, or human-in-the-loop annotation workflows; familiarity with modern AI tools/platforms (e.g., Databricks, PyTorch, Hugging Face, LangChain, LangGraph, Haystack, MLflow).

Nice to have

• Experience in life sciences/pharmaceuticals/chemistry/biomedical research and clinical data.
• Familiarity with ontologies, taxonomies, controlled vocabularies, and metadata standards.
• Exposure to production ML systems, MLOps, data pipelines, and model monitoring.

Culture & Benefits

• Healthy work/life balance with flexible working hours.
• Wellbeing initiatives, shared parental leave, study assistance, and sabbaticals.
• Emphasis on scientific rigor, collaboration, responsible AI, customer focus, and continuous improvement.