Data Scientist – Decision Science & Modelling

Job Description

A global healthcare technology and research company is hiring a Data Scientist – Decision Science & Modelling to develop analytical models and decision-support systems that improve business performance and strategic decision-making. This role is ideal for professionals with strong quantitative skills who enjoy solving complex real-world problems using data, statistical modelling, machine learning, and simulation techniques. The successful candidate will collaborate with engineering, product, and business teams to deliver scalable analytical solutions.

Location: London, UK (Remote Eligible)
Job Type: Full-time

Key Responsibilities

• Develop, maintain, and improve statistical, probabilistic, and simulation-based models.
• Translate business challenges into practical analytical and modelling solutions.
• Build decision-support tools that enhance operational and commercial decision-making.
• Design, execute, and evaluate experiments to measure model performance and business impact.
• Work with incomplete or evolving datasets while quantifying uncertainty and validating model outputs.
• Collaborate with engineering teams to support the deployment of analytical models into production.
• Monitor model performance, identify model drift, and recommend improvements where necessary.
• Present technical findings, assumptions, and recommendations clearly to both technical and non-technical stakeholders.

Requirements

• Degree in Data Science, Statistics, Mathematics, Economics, Operational Research, Computer Science, or another quantitative discipline.
• Strong experience developing analytical models for business or operational decision-making.
• Proficiency in Python or R for data science and model development.
• Solid understanding of statistical modelling, machine learning, forecasting, and decision science.
• Experience working with imperfect, incomplete, or uncertain datasets.
• Strong analytical thinking and problem-solving skills.
• Excellent communication skills with the ability to explain technical concepts to non-technical audiences.
• Experience supporting the deployment of analytical models into production environments is an advantage.
• Knowledge of Bayesian inference, optimization, simulation modelling, synthetic data generation, or generative AI is beneficial.
• Experience working alongside software engineering and product teams is desirable.