Associate Data Scientist, Concirrus

• You’re equally comfortable building traditional ML models and experimenting with modern GenAI approaches
• You’ve delivered predictive models end-to-end, and you’re curious about leveraging LLMs/SLMs for domain-specific applications
• You understand data quality challenges, hypothesis validation, and how to move research prototypes into robust production
• You’re motivated, detail-oriented, and see the bigger picture of applying advanced data methods to business value
• Degree in Maths, Computer Science, Data Science, or related field
• Professional experience as a Data/ML Scientist, including end-to-end ML lifecycle
• Strong Python skills, including pandas, numpy, scikit-learn, and exposure to deep learning frameworks (PyTorch/TensorFlow)
• Experience building and deploying predictive models (classification, regression, clustering)
• Strong data wrangling, feature engineering, and problem-solving skills
• Familiarity with embeddings, semantic similarity, or search techniques
• Excellent self-management, prioritisation, and communication skills

Desirable

• We are seeking a Data Scientist to join our rapidly expanding London-based team
• You will be hands-on in developing and deploying ML and GenAI-driven solutions
• Beyond predictive modelling, you’ll explore RAGs, semantic search, small language models (SLMs), embeddings, and fine-tuning techniques to maximise the value of diverse datasets, from commercial insurance to global sensor data
• You’ll thrive interrogating structured and unstructured data, designing pipelines, and experimenting with new approaches that push the frontier of applied data science
• Work in a multi-disciplined team to deliver projects according to roadmap priorities
• Analyse and model structured and unstructured data using advanced ML and GenAI techniques
• Design, implement, and evaluate models end-to-end: data ingestion, feature engineering, model training, validation, deployment, and monitoring
• Apply RAG architectures, vector databases, embeddings, and semantic search to enrich products with context-aware AI
• Fine-tune and optimise SLMs/LLMs for domain-specific tasks such as document classification, summarisation, and risk analysis
• Research and prototype new approaches (e.g., prompt engineering, knowledge graph integration, hybrid search) to enhance product performance
• Collaborate with Product teams to embed GenAI capabilities into Quest and generate new product opportunities
• Languages: Python, SQL, exposure to TypeScript or Java is a plus
• ML/AI: scikit-learn, PyTorch, TensorFlow, Hugging Face, LangChain
• Data: Apache Spark (PySpark), pandas, polars, numpy
• GenAI-specific: Vector databases (FAISS, Pinecone, Weaviate), embeddings, RAG pipelines, fine-tuning frameworks
• Cloud & Infra: GCP/Azure/AWS, Linux, distributed systems