Senior Data Scientist MMH261028-7

Role Purpose To apply advanced analytics, statistical modelling, machine learning and investment\-domain insight to solve complex business problems, improve investment and client outcomes, and accelerate the organisation’s digital transformation. The role converts data into scalable intelligence, decision\-support tools and digital solutions that enable investment professionals, distribution teams, operations and clients to make faster, better\-informed decisions. **Requirements** * Bachelor’s degree in data science, Statistics, Mathematics, Computer Science, Engineering, Econometrics, Quantitative Finance or a related quantitative discipline. * A postgraduate qualification in a relevant discipline is advantageous. * 5 to 7 years’ relevant experience in data science, advanced analytics or machine learning; the required depth may vary with role level. * Demonstrated experience delivering analytical models or data products beyond proof\-of\-concept stage. * Experience in investments, asset management, wealth management, financial services or another regulated environment is strongly preferred. * Experience working in agile, multidisciplinary digital or product teams would be an advantage. Duties and Responsibilities **Data Science and Advanced Analytics** * Frame ambiguous business challenges as clear analytical questions, hypotheses and measurable outcomes. * Explore, clean, combine and analyse structured and unstructured data from internal and external sources. * Develop, validate and maintain statistical, machine\-learning and optimisation models. * Apply techniques such as segmentation, propensity modelling, forecasting, anomaly detection, natural language processing and recommendation methods where appropriate. * Design robust experiments and evaluate the causal and commercial impact of digital interventions. * Translate complex analysis into clear recommendations, visual narratives and decision\-ready insights. Investment and Client Insight * Partner with investment professionals to identify opportunities where data can strengthen research, portfolio insight, risk awareness and decision support. * Analyse client, adviser, product, market and transactional data to identify behavioural patterns, needs and emerging opportunities. * Support the development of personalised propositions, next\-best\-action capabilities and digitally enabled investment experiences. * Contribute analytical insight to product development, distribution effectiveness, client retention and operational optimisation. * Ensure that model outputs are interpreted within the relevant investment, market and client context. **Data Products and Digital Delivery** * Work in cross\-functional product teams with data engineers, software engineers, designers, product owners, architects and business specialists. * Convert prototypes into maintainable, monitored and reusable analytical services or data products. * Define model features, interfaces, acceptance criteria and monitoring requirements for deployment. * Apply sound software\-engineering practices, including version control, testing, documentation and peer review. * Monitor model performance, drift, data quality and business impact, and initiate enhancements where required. * Contribute to reusable analytical assets, standards and communities of practice. * Design, develop and operationalise Agentic AI solutions and agentic workflows that can reason, plan and execute defined tasks, incorporating appropriate human oversight, testing, monitoring, security and governance controls. **Governance, Risk and Responsible AI** * Comply with data privacy, information security, model governance, regulatory and records\-management requirements. * Document data lineage, assumptions, limitations, validation results and intended use of analytical models. * Assess models for bias, fairness, explainability, robustness and unintended consequences. * Protect confidential client, investment and business information and use only approved data and technology environments. * Escalate material data\-quality, model\-risk or ethical concerns through the appropriate governance channels. **Stakeholder Partnership and Influence** * Build trusted partnerships across investments, product, distribution, operations, technology, risk, compliance and data functions. * Facilitate discovery sessions to understand user needs, prioritise use cases and define value. * Communicate technical concepts in accessible business language and constructively challenge assumptions. * Promote data literacy, analytical thinking and evidence\-based decision\-making. * Coach less experienced team members and share knowledge across the digital capability. **Decision\-Making and Accountability** * Recommend appropriate analytical approaches, modelling techniques and evaluation methods. * Determines whether data is fit for the intended analytical purpose and highlights material limitations. * Balances model performance, interpretability, implementation effort, risk and user value. * Owns the quality and reproducibility of assigned analytical work and supports its performance in production. * Works within delegated authority and approved governance frameworks; material investment, client or risk decisions remain with the accountable business owner. 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