Senior Data Scientist MMH261028-7

Momentum; Centurion; Sur place; ZAR 850 000 - 1 150 000; Plein temps

Résumé du poste

Momentum is seeking a Data Scientist to apply advanced analytics, statistical modelling and ML to investment and client problems. You will transform data into scalable intelligence, decision-support tools and digital solutions for investment professionals, distribution teams and clients.

The role emphasizes cross-functional collaboration with data engineers, product owners and business leaders, applying responsible AI and governance practices while driving measurable business outcomes.

Qualifications

• Frame ambiguous business problems into clear analytical questions with measurable outcomes.
• Develop, validate and maintain analytical models and data products.
• Translate complex analysis into actionable business insights.

Responsabilités

• Apply advanced analytics, statistics, ML to solve investment and client problems.
• Collaborate with investment, distribution, operations and tech teams.
• Build scalable data products and decision-support tools.
• Ensure governance, bias checks and explainability of models.
• Design experiments and assess business impact of interventions.
• Maintain performance monitoring and data quality controls.

Connaissances

Statistical modelling

Machine learning

Data analysis

Data storytelling

Cross-functional collaboration

Formation

Bachelor’s degree in data science or related quantitative discipline

Postgraduate qualification advantageous

Description du poste

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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