Research Scientist (Information Retrieval & Recommendation Systems)

Job Description Research, evaluate, and adapt state-of-the-art techniques to solve large-scale AI problems. Fine-tune and evaluate Transformer-based models and embedding models for domain-specific applications. Contribute to research initiatives in Information Retrieval, Product Matching, Recommendation Systems, and Generative AI. Collaborate with technical colleagues on the integration of ML solutions. Contribute to scientific publications, patents, and innovation initiatives. Maintain software and data assets following high-quality engineering standards Develop and apply machine learning innovations to business problems with moderate technical supervision. Assess experimental results and determine their applicability to real-world business challenges. Understand stakeholder requirements and communicate results and recommendations clearly and effectively. Perform feasibility studies and analyze data to identify appropriate technical solutions. Develop scalable, reproducible, and maintainable machine learning solutions. Qualifications Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Mathematics, Physics, or a related quantitative discipline. 3-6 years of experience in Machine Learning, Deep Learning, or AI research. Experience with Python, PyTorch, Hugging Face, Pandas, Scikit-learn, and Git. Experience training Transformer models, embeddings, and modern NLP techniques. Experience training custom SLMs and /or small reasoning models. Familiarity with Information Retrieval concepts and vector databases (e.g., FAISS, ChromaDB, Pinecone). Understanding of contrastive learning and representation learning techniques. Experience performing EDA with large datasets and writing production-quality code. Ability to understand scientific papers and implement research ideas into practical solutions. Strong analytical, problem-solving, and communication skills. Ability to work effectively within a diverse, international team. Good to Have: Publications in AI, NLP, Information Retrieval, or Recommendation Systems. Experience with RAG architectures, LLMs, and agent-based frameworks. Experience with LangChain or LangGraph. Experience with Knowledge Graphs. Experience with MLOps practices. Experience with Databricks, SQL, Elasticsearch. Experience in retail, consumer intelligence, ecommerce, or FMCG domains. Company Description #LI-hybrid We are a team of passionate, curious and diverse professionals who care about each other, our work, our company and the world. We are looking for someone who combines skill in machine learning with an ability to connect with people and see the wider context for a range of business problems. You will be responsible for assessing state-of-the-art techniques, researching novel ideas to improve them, and applying them to large scale problems, mainly in the domain of recommender systems and NLP (from semantic search to deep research agents). To solve problems effectively, you will need to be able to build relationships and understand the business context in which we are operating. This position will suit someone who has a deep understanding of machine learning and is comfortable presenting clearly and winsomely to a non-technical audience. NielsenIQ helps companies to meet the needs of people all over the world for food, health, hygiene, tech and durable products. Since 1923, NIQ has moved measurement forward for industries and economies across the globe. This position opens the opportunity to research and apply state-of-the-art AI/ML techniques to strategic global projects. Additional Information Salary: €46100 up to €51000 EUR gross per year. This role might also be eligible for a performance-based bonus. Placement within the range will depend on objective criteria including experience, skills, and internal equity considerations. We are committed to equal pay and pay transparency. Remuneration decisions are made using gender-neutral and objective criteria.​​ Our Benefits Flexible working environment Volunteer time off LinkedIn Learning Employee-Assistance-Program (EAP) NIQ may utilize artificial intelligence (AI) tools at various stages of the recruitment process, including résumé screening, candidate assessments, interview scheduling, job matching, communication support, and certain administrative tasks that help streamline workflows. These tools are intended to improve efficiency and support fair and consistent evaluation based on job-related criteria. All use of AI is governed by NIQ’s principles of fairness, transparency, human oversight, and inclusion. Final hiring decisions are made exclusively by humans. NIQ regularly reviews its AI tools to help mitigate bias and ensure compliance with applicable laws and regulations. If you have questions, require accommodations, or wish to request human review were permitted by law, please contact your local HR representative. For more information, please visit NIQ’s AI Safety Policies and Guiding Principles: https://nie