Data Science Manager

ph3Job Description /h3 pAre you a (Senior) Data Science Manager and eager to consider a new opportunity ? /p pHow about a job opportunity at a Data Analytics Publishing company in Amsterdam or London? /p h3General Information /h3 ul liDuration: 12 month contract or indefinite from the start /li liNo. of working hours: 36 hours per week /li liLocation: 1 / 2 days per week in Amsterdam or London /li liContract type: FTE, Direct contract with client /li liOther: You need to be a resident of NL OR the UK at time of application /li liAvailable salary: Data Science manager: 80.000 – 131.000k in either Euro or GBP /li /ul pSenior DS Manager: 100.000 – 167.000 in either Euro or GBP /p pWe 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. /p pThis 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. /p pThe 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. /p pYou will collaborate closely with product leadership, software engineering teams, subject matter experts, and business stakeholders to deliver scalable, measurable, production-grade AI solutions. /p h3Responsibilities /h3 h3Leadership Team Development (60%) /h3 ul liLead, mentor, and support a team of data scientists, enabling technical excellence and professional growth. /li liDefine team objectives, priorities, and delivery plans aligned with organizational strategy. /li liAllocate resources across multiple initiatives while balancing short-term delivery and long-term innovation. /li liFoster a culture of collaboration, experimentation, responsible AI practices, customer focus, and continuous improvement. /li liEstablish and maintain best practices for experimentation, model development, evaluation methodologies, and collaboration with engineering teams. /li /ul h3Data Science AI Delivery (40%) /h3 ul liGuide 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. /li liOversee systems supporting tasks such as: ul lidocument classification /li liinformation extraction /li lientity recognition /li lientity linking /li lisemantic enrichment /li liranking and retrieval /li liprediction and decision support /li /ul /li liDrive initiatives that integrate structured and unstructured data from multiple scientific and technical sources. /li liEvaluate 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. /li liPartner with engineering teams to ensure solutions are scalable, maintainable, reliable, and production-ready. /li /ul h3Evaluation Quality /h3 ul liDefine evaluation frameworks for machine learning models, search systems, NLP pipelines, and AI-enabled product capabilities. /li liEstablish metrics for retrieval quality, model performance, ranking effectiveness, data accuracy, user experience, and business outcomes. /li liLead offline evaluation, A/B testing, annotation processes, error analysis, and human-in-the-loop validation activities. /li liPromote responsible AI principles including transparency, fairness, explainability, privacy, governance, and risk mitigation. /li liEncourage evidence-based decision making supported by clear communication of findings and recommendations. /li /ul h3Cross-Functional Collaboration /h3 ul liWork closely with product managers, engineers, domain specialists, information architects, data experts, and commercial stakeholders. /li liTranslate business objectives into measurable AI initiatives and delivery roadmaps. /li liPresent technical concepts, trade-offs, risks, and recommendations to both technical and executive audiences. /li liContribute to the broader AI strategy and represent the data science function within cross-functional planning activities. /li /ul h3Required Qualifications /h3 ul liMaster'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. /li liAt least 3 years of experience leading or managing technical teams. /li liExperience reviewing technical work, mentoring data scientists, and improving engineering and data science practices. /li liAt least 5 years of experience in data science: ul liapplied machine learning, python, data science libraries, natural language processing, statistical modelling, information retrieval, or AI solution development. /li /ul /li liStrong understanding of experimentation, supervised and unsupervised learning techniques, statistical analysis, model evaluation, experimentation, and generative AI concepts. /li liExperience working with large-scale structured and unstructured datasets. /li liHands-on experience with large language models, embedding techniques, retrieval-augmented generation, AI evaluation methodologies, and human-assisted annotation workflows. /li liFamiliarity with modern AI development ecosystems and frameworks such as deep learning libraries, experiment tracking platforms, orchestration frameworks, vector search technologies, or equivalent tools. /li /ul pYou can check other job opportunities in our website: Jobs – Magno IT. /p /p #J-18808-Ljbffr