Senior Data Scientist

Introduction Through our client-facing brands and our other specialist brands, Momentum Group enables business and people from all walks of life to achieve their financial goals and life aspirations.

We help people grow their savings, protect what matters to them and invest for the future. We help companies and organisations care for and reward their employees and members. Through our own network of advisers or via independent brokers and utilising new platforms Momentum Group provides practical financial solutions for people, communities and businesses.

Disclaimer As an applicant, please verify the legitimacy of this job advert on our company career page.

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 & 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.

Competencies

Technical Capabilities

• Advanced proficiency in Python and SQL; working knowledge of R is advantageous.
• Strong grounding in statistics, machine learning, feature engineering, model validation and experimental design.
• Experience with data visualisation and business-intelligence tools.
• Working knowledge of cloud data and machine-learning platforms, data pipelines and modern analytical environments.
• Familiarity with APIs, containerisation, automated testing, version control and machine-learning operations practices.
• Ability to work with large, complex and imperfect datasets while maintaining data quality and reproducibility.
• Understanding investment concepts, financial markets, products, portfolio data or client investment behaviour would be an advantage.
• Knowledge of generative AI, large language models and unstructured-data techniques is advantageous where supported by appropriate controls.

Behavioural Capabilities

• Strategic and commercial thinking: Connects analytical work to client value, investment outcomes and business priorities.
• Intellectual curiosity: Explores patterns, tests assumptions and continuously learns.
• Problem solving: Structures complex problems and applies pragmatic, evidence-based judgement.
• Communication and influence: Makes complex concepts understandable and builds confidence in recommendations.
• Collaboration: Works effectively across disciplines and values diverse expertise.
• Delivery orientation: Moves from insight to implementation and maintains focus on measurable outcomes.
• Client centricity: Designs with the needs, context and fair treatment of clients in mind.
• Integrity and risk awareness: Handles data responsibly and raises concerns early.
• Adaptability: Operates effectively in a fast-changing digital and investment environment.

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