Data Scientist - Machine Learning
IQVIA
London
Data Scientist ACOE, Patient & Practitioner Analytics Paddington, London (Hybrid) We are looking for a creative, innovative, intellectually curious and entrepreneurial Data Scientist with experience in developing Machine Learning solutions to join our London-based team. This is an exciting opportunity to work in one of the world's leading human data science teams working with Real World Insights to help our clients answer specific questions globally, make more informed decisions and deliver results. The role We are looking for a Data Scientist who will play a pivotal role in designing and implementing advanced analytical solutions for complex challenges in the life science domains. The primary focus of the role will be to design and apply cutting-edge machine learning techniques to extract meaningful insights from large-scale, real-world medical data, helping to inform and improve healthcare outcomes. This role requires a strong ability to interpret diverse datasets, identify the most suitable analytical approaches, and deliver robust solutions to tackle multifaceted business problems. You will be using existing machine learning tools and will be responsible for tailoring them as business problems require. As part of this role, you will also be interacting with internal and external stakeholders from different backgrounds, therefore good technical and non-technical communication skills are crucial. In addition, the role offers a scope for development in other key areas
- Product Development – collaborating with software engineers, system architects, and fellow data scientists to build scalable, automated routines and libraries.
- Business Development – contribute to business development initiatives through technical support for demos, thought leadership (e.g. publications, academic presentations, workshops), and input on project proposals.
- Client Engagement – engage in delivering tailored solutions, generating reports, and communicating findings effectively to meet client needs.
- MSc or PhD in Machine Learning, Computer Science, Physics, Bioinformatics or similar.
- Analytical experience within a top academic or commercial organisation where data-led problem solving was a core part of the role.
- Experience with the design and delivery of advanced machine learning projects in either academic or commercial settings.
- Experience solving problems within commercial life science and healthcare (e.g. prediction of health outcomes such as disease progression, line of therapy, market sizing, etc.)
- A proactive, innovative and pragmatic approach to problem solving and an ability to think critically and independently. Ability to work as part of a cross-functional team.
- Experience with software development lifecycle, particularly using GitLab.
- Experienced in handling Big Data.
- Deep knowledge of machine learning methods, particularly gradient boosting classifiers. (e.g. XGBoost, LightGBM)
- Capable of developing and using AutoML tools.
- Well versed in explainability methods. (e.g. SHAP, LIME, ELI5)
- Skilled in Python/R and SQL.
- Proficient in using data science packages (e,g sklearn, pandas, matplotlib, etc.)
- Exposure to GenAI technologies, including but not limited to prompt engineering and Agentic AI.
- Ability to write clean and documented code, as well as understanding and modifying existing code bases.
- Demonstrated ability to communicate complex analytical concepts and insights to both technical and non-technical audience.
- Demonstrated creativity, critical thinking, project planning, and attention to detail capabilities.
- Knowledge of pharmaceutical market and experience with pharmaceutical data. (medical, hospital, pharmacy, claims data)
- Familiar with using Linux terminal & containers. (e.g. Docker)
- Knowledge of deep learning methods. (e.g. Convolutional neural networks, Recurrent neural networks, Transformer)
- Knowledge of epidemiology / biostatistics, particularly analytical issues relating to studies of treatment effectiveness, disease progression, adherence, healthcare utilization, etc.
- Knowledge of healthcare / life science issues involving Real-World Evidence.
Posted 2025-09-21
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