Data Scientist – Decision Science & Modelling

Job Description We are seeking a Data Scientist to design and deliver analytical models and decision-support systems that improve understanding, prediction and decision-making across the business. The role focuses on building practical models of complex real-world systems, working with imperfect data, uncertainty and competing objectives to generate commercially valuable outcomes. Responsible for developing deployable analytical solutions in partnership with Engineering teams, while not owning production infrastructure or application development. Key Responsibilities: Model Development Design, develop and maintain statistical, probabilistic and simulation-based models. Translate complex business questions into tractable modelling problems. Select appropriate modelling approaches based on the characteristics of the problem rather than methodological preference. Develop prototypes and working solutions iteratively, refining approaches as new information becomes available. Build analytical assets that can be reused as products, decision-support tools or operational capabilities. Design and analyse experiments to evaluate interventions, operational changes and model effectiveness. Inference & Uncertainty Work effectively with incomplete, imperfect and evolving datasets. Develop approaches for estimating missing information and combining evidence from multiple sources. Quantify uncertainty and communicate appropriate confidence in model outputs. Test assumptions and identify limitations within modelling approaches. Optimisation & Decision Support Develop frameworks that improve operational and commercial decision-making. Evaluate alternative actions, trade-offs and potential outcomes. Support automation of appropriate decision processes through analytical models. Design experiments and simulations that inform strategic and operational choices. Validation & Quality Validate models using appropriate testing, back-testing and comparison techniques. Assess sensitivity to assumptions and changing conditions. Monitor model performance over time and identify concept drift or degradation. Maintain high standards of analytical rigour and reproducibility. Collaboration & Communication Partner closely with Business Analysts, Engineering, Product and operational teams. Explain modelling approaches, assumptions and results clearly to non-technical audiences. Document methodologies, limitations and recommendations in a practical and accessible way. Contribute to a culture of experimentation and evidence-based decision-making. Productisation & Engineering Partnership Design models and analytical approaches with operational deployment in mind. Work closely with Engineering teams to translate models into scalable production solutions – Data Science defines the models while Engineering owns production implementation. Define model inputs, outputs, assumptions and performance requirements required for implementation. Support the development of APIs, services or analytical components by providing technical guidance and validation. Contribute to testing and acceptance of implemented solutions to ensure behaviour aligns with model expectations. Help define monitoring, evaluation and retraining requirements where appropriate. Partner with Product, Engineering and Business teams to ensure analytical solutions deliver measurable business value. Qualifications Essential Strong grounding in applied statistics, modelling, data science, operational research, economics, mathematics or a related quantitative discipline. Experience building models that support real-world decisions, products or operational processes. Experience working with uncertainty, incomplete information and imperfect datasets. Ability to move from loosely defined problems to practical analytical solutions. Strong problem decomposition and structured thinking skills. Ability to communicate technical concepts clearly to non-technical stakeholders. Strong coding skills in Python or R. Experience developing analytical solutions in code rather than primarily through spreadsheet-based analysis. Experience taking analytical models from prototype through to operational deployment. Core Technical Skills Candidates should demonstrate strength in several of the following areas: Statistical modelling Probabilistic modelling Bayesian inference Forecasting Machine learning Simulation modelling Agent-based modelling Optimisation techniques Experimental design Synthetic data generation Decision science Scenario analysis As important as experience with any individual technique is the ability to understand trade-offs and select the most appropriate approach for the problem being solved. Experience or familiarity with the following is desirable Building simulation or digital twin style systems. Optimisation, operational research or decision science techniques. Working with survey, panel or market research data. Applying machine learning or AI techniques to business problems. Model monitoring