DATA SCIENTIST – DECISION SCIENCE & MODELLING

<p>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.</p><p>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.</p><p>Responsible for developing deployable analytical solutions in partnership with Engineering teams, while not owning production infrastructure or application development.</p><p><strong>Key Responsibilities:</strong></p><p>Model Development</p><ul><li>Design, develop and maintain statistical, probabilistic and simulation-based models.</li><li>Translate complex business questions into tractable modelling problems.</li><li>Select appropriate modelling approaches based on the characteristics of the problem rather than methodological preference.</li><li>Develop prototypes and working solutions iteratively, refining approaches as new information becomes available.</li><li>Build analytical assets that can be reused as products, decision-support tools or operational capabilities.</li><li>Design and analyse experiments to evaluate interventions, operational changes and model effectiveness.</li></ul><p>Inference &amp; Uncertainty</p><ul><li>Work effectively with incomplete, imperfect and evolving datasets.</li><li>Develop approaches for estimating missing information and combining evidence from multiple sources.</li><li>Quantify uncertainty and communicate appropriate confidence in model outputs.</li><li>Test assumptions and identify limitations within modelling approaches.</li></ul><p>Optimisation &amp; Decision Support</p><ul><li>Develop frameworks that improve operational and commercial decision-making.</li><li>Evaluate alternative actions, trade-offs and potential outcomes.</li><li>Support automation of appropriate decision processes through analytical models.</li><li>Design experiments and simulations that inform strategic and operational choices.</li></ul><p>Validation &amp; Quality</p><ul><li>Validate models using appropriate testing, back-testing and comparison techniques.</li><li>Assess sensitivity to assumptions and changing conditions.</li><li>Monitor model performance over time and identify concept drift or degradation.</li><li>Maintain high standards of analytical rigour and reproducibility.</li></ul><p>Collaboration &amp; Communication</p><ul><li>Partner closely with Business Analysts, Engineering, Product and operational teams.</li><li>Explain modelling approaches, assumptions and results clearly to non-technical audiences.</li><li>Document methodologies, limitations and recommendations in a practical and accessible way.</li><li>Contribute to a culture of experimentation and evidence-based decision-making.</li></ul><p>Productisation &amp; Engineering Partnership</p><ul><li>Design models and analytical approaches with operational deployment in mind.</li><li>Work closely with Engineering teams to translate models into scalable production solutions – Data Science defines the models while Engineering owns production implementation.</li><li>Define model inputs, outputs, assumptions and performance requirements required for implementation.</li><li>Support the development of APIs, services or analytical components by providing technical guidance and validation.</li><li>Contribute to testing and acceptance of implemented solutions to ensure behaviour aligns with model expectations.</li><li>Help define monitoring, evaluation and retraining requirements where appropriate.</li><li>Partner with Product, Engineering and Business teams to ensure analytical solutions deliver measurable business value.</li></ul> <p><strong>Essential</strong></p><ul><li>Strong grounding in applied statistics, modelling, data science, operational research, economics, mathematics or a related quantitative discipline.</li><li>Experience building models that support real-world decisions, products or operational processes.</li><li>Experience working with uncertainty, incomplete information and imperfect datasets.</li><li>Ability to move from loosely defined problems to practical analytical solutions.</li><li>Strong problem decomposition and structured thinking skills.</li><li>Ability to communicate technical concepts clearly to non-technical stakeholders.</li><li>Strong coding skills in Python or R.</li><li>Experience developing analytical solutions in code rather than primarily through spreadsheet-based analysis.</li><li>Experience taking analytical models from prototype through to operational deployment.</li></ul><p><strong>Core Technical Skills</strong></p><p>Candidates should demonstrate strength in several of the following areas:</p><ul><li>Statistical modelling</li><li>Probabilistic modelling</li><li>Bayesian inference</li><li>Forecasting</li><li>Machine learning</li><li>Simulation modelling</li><li>Agent-based modelling</li><li>Optimisation techniques</li><li>Experimental design</li><li>Synthetic data generation</li><li>Decision science</li><li>Scenario analysis</li></ul><p>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.</p><p><strong>Experience or familiarity with the following is desirable</strong></p><ul><li>Building simulation or digital twin style systems.</li><li>Optimisation, operational research or decision science techniques.</li><li>Working with survey, panel or market research data.</li><li>Applying machine learning or AI techniques to business problems.</li><li>Model monitoring, governance and validation practices.</li><li>Contributing to analytical products rather than one-off analyses.</li><li>Modern AI and generative AI approaches.</li><li>Working in multidisciplinary teams alongside software engineers and product teams.</li><li>Software development lifecycles and productionisation of analytical solutions.</li><li>Defining requirements and acceptance criteria for model implementation</li></ul> <p><strong>Employee Benefits:</strong></p><ul><li>25 days annual leave</li><li>Participation in a company bonus scheme linked to personal and company performance</li><li>Group Life Cover 4x salary</li><li>Pension 4%/4% employee/employer contributions</li><li>Vitality after probation</li><li>Staff discount scheme</li><li>Discounted gym membership​</li></ul><p>#LI-LC1<br> #LI-Remote</p>