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Job description
Overview
Join a pioneering team in London/Oxford focused on leveraging machine learning, natural language processing, and generative AI to accelerate scientific discovery and improve decision-making. This role involves transforming complex scientific and business needs into practical, production-grade AI solutions that deliver significant user benefits.
About the Team
Our globally distributed team supports a stable suite of products, including educational tools and electronic health records, aimed at facilitating digital documentation and training for modern clinical environments. We prioritize trust, respect, collaboration, agility, and quality in our work culture.
Key Responsibilities
- Develop machine learning, NLP, and generative AI models that enhance scientific research, knowledge extraction, decision support, and intelligent content interpretation.
- Handle diverse and complex scientific data such as publications, datasets, knowledge graphs, ontologies, taxonomies, citations, metadata, and multidisciplinary content.
- Apply appropriate techniques including classification, regression, clustering, ranking, feature engineering, deep learning, embeddings, retrieval methods, large language models (LLMs), and generative AI to solve problems.
- Create solutions for semantic search, information retrieval, entity extraction, classification, recommendation systems, ranking, summarization, question answering, and evidence-based content generation.
- Build, test, fine-tune, prompt, and deploy models as reliable production-ready systems while enhancing quality and user impact.
- Write clean, well-tested Python code contributing reusable components and scalable pipelines for preprocessing, inference, experimentation, monitoring, and continuous system improvement.
- Manage deployment processes, monitor models, detect data drift, automate retraining, and optimize ongoing performance of data science applications.
- Collaborate cross-functionally with engineering, product, UX, analytics, research, and domain experts to translate technical concepts for varied audiences and integrate feedback effectively.
Candidate Requirements
- Educational background or experience in data science, machine learning, AI, NLP, statistics, applied math, computer science, or related quantitative disciplines.
- Hands-on experience with state-of-the-art large language models such as OpenAI GPT, Anthropic Claude, Google Gemini, including fine-tuning of LLMs or smaller language models (SLMs).
- Proficiency in Python programming with strong practices for clean, maintainable, and thoroughly tested code.
- Solid knowledge of machine learning principles covering supervised and unsupervised learning, feature engineering, evaluation methods, model selection, and performance metrics.
- Experience handling various data types—structured, semi-structured, and unstructured—particularly large text or content-rich datasets.
- Familiarity with fundamental data science and ML libraries like Pandas, NumPy, SciPy, Scikit-learn, PyTorch, TensorFlow, and Matplotlib.
- Exceptional analytical and problem-solving skills with an ability to transform ambiguous requirements into concrete, data-driven solutions with a focus on quality.
- Strong communication abilities, collaborative mindset, and motivation to build robust systems that offer real value to users.
Work Environment and Benefits
The company supports a balanced work-life approach, offering flexible working hours to accommodate productivity patterns and personal commitments. Employees have access to wellness initiatives, parental leave, study support, and sabbatical opportunities to encourage long-term career and personal growth.
About the Company
A global frontrunner in information and analytics, the employer helps researchers and healthcare professionals push scientific boundaries and enhance health outcomes worldwide. Combining extensive data with advanced analytics, the company underpins groundbreaking science, education, healthcare improvement, and sustainability efforts, leveraging innovative technologies to promote a better future.