Junior Quantitative Analyst

Let's Write Africa's Story Together!

Old Mutual is a firm believer in the African opportunity and our diverse talent reflects this.

Job Description

A junior quantitative / data analyst at Old Mutual Limited (OML), embedded in the Group Risk Quant Team within the second line risk function. The role provides foundational data engineering, analytics, and quantitative support across the team's portfolio of model validation, model development, model risk oversight, and broader Group Risk projects.

The role is positioned to develop into a fully-fledged quantitative analyst over time, while delivering immediate value through high-quality data extraction, processing, transformation, analytics and BI/dashboarding work that underpins the team's deliverables, as well as assistance to more senior quants in technical/statistical work.

Primary responsibilities

Data

• Data extraction from a range of source systems, repositories and reports (internal data warehouses, MIMS/model inventory, market data, business unit submissions, and ad-hoc files from data providers), using SQL and other extraction tools.

• Design, build, and maintenance of data processing workflows in Alteryx, including ingestion, cleansing, joins, transformations, business-rule application, and exception handling.

• Development of Python scripts for data manipulation, automation, statistical analysis, and reusable analytics components within the team's code repository.

• Data quality assurance: reconciliation, completeness, accuracy, timeliness and validity checks; exception reporting; feedback loops to data providers; tracking and remediation of recurring issues.

• Data transformation and enhancement: enrichment from reference data, derivation of analytical attributes, mapping/normalisation, and preparation of model-ready and report-ready datasets.

• Design, build and maintenance of Tableau dashboards and other BI outputs supporting the team and stakeholders, including data model design, calculations, visual design, publishing, access management and ongoing refresh/maintenance.

• Documentation of data sources, SQL queries, transformations, scripts, dashboards, and assumptions to support reproducibility, peer review and audit-readiness.

Model development and validation support

• Hands-on data and analytical support to quantitative analysts on model development and model validation projects across retail lending, investment credit, market, liquidity, financial, and other model types

• Preparation of validation datasets, development of analytical tables, replication of model outputs, segmentation analysis, performance and stability testing, and packaging of results for inclusion in validation reports

• Build and maintenance of reusable code, templates and challenger components in the team's knowledge repository to accelerate future model development and validation work

• Drafting of clearly written, well-structured technical sections within model development and validation deliverables, under guidance of the lead quant on each project

Quant, data and statistical support to other Group Risk teams

• Provision of ad-hoc quantitative, data and statistical support to other Group Risk teams (e.g. Financial Risk, Credit, Compliance, Operational Risk, Insurance Risk) on data sourcing, data quality, analytics, reporting automation, and basic statistical investigations

• Contribution to cross-team Group Risk data and reporting projects (such as Graphite-type initiatives), including ETL build, transformation logic, exception/error reporting, and dashboard delivery

• Identification of common data requirements and reuse opportunities across projects, aligned to best practice in data modelling (e.g. UDM, IIA) and to the requirements of the Data Risk Policy

Model risk oversight

• Administration and ongoing maintenance of the Group's model inventory and management system (e.g. MIMS), including data captures, updates, user access, configuration support, and quality control of inventory data

• Support to the model risk policy attestation cycle: distribution of attestation instruments, tracking of responses, consolidation of evidence, exception reporting, and preparation of attestation summaries for governance forums

• Operational support to model risk governance processes, including scheduling, agenda packs, minute-taking support, action tracking, and maintenance of validation schedules

• Input into relevant frameworks, policies, standards and templates from a data and analytics perspective

Secondary responsibilities

While the primary responsibilities focus on data, analytics, model support and model risk administration, secondary responsibilities are included to provide variability and personal growth opportunities to the candidate, and to develop a cross-functional team that is not key person dependent.

• Continuous learning and skills development: Active, structured development across quantitative techniques, programming and tooling (Python, SQL, Alteryx, Tableau), different risk types and different business units, with progression toward independent ownership of small validation or modelling projects under supervision. Pursuit of relevant certifications, courses and reading in line with personal development plans agreed with the Head of Group Risk Quant.

• Knowledge sharing: Proactive sharing of insights, techniques, reusable code and lessons learned within the team and with broader Group Risk stakeholders. Participation in peer code reviews, technical walkthroughs and mentoring of graduates or interns on rotation.

• Contribution to team workshops and brainstorming: Active participation in team workshops, technical deep-dives and brainstorming sessions, contributing ideas, alternative approaches and structured problem-solving input on team challenges, methodology design, and process improvement.

• Promotion of the team and its services: Support the visibility and reputation of the Group Risk Quant team by contributing to internal communications, showcases, demos and stakeholder engagements that articulate the team's capabilities, deliverables and value proposition across OML.

• Networking with quant and technical teams across OML: Build and maintain working relationships with quant, data, analytics, technology and BI communities across OML business units and second-line functions, to enable knowledge exchange, reuse of methods and tools, and identification of collaboration opportunities.

• Maintenance of the quant knowledge repository and case studies: Contribute to the upkeep of the Group Risk Quant knowledge repository, including code libraries, SQL templates, methodology notes, validation patterns and documentation standards. Prepare written case studies of completed projects (development, validation, data and analytics) for inclusion in the repository to support reuse, onboarding and team learning.

• Fostering a culture of teamwork: Actively contribute to a collaborative, respectful and inclusive team culture, characterised by mutual support, constructive challenge, shared ownership of outcomes, and a "no key-person" mindset where work is documented, transferable and resilient to individual absences.

Minimum Experience/Qualifications (Required for the Job)

Experience

• At least 1–3 years' experience in a data, analytics, BI, or quantitative role within a banking, insurance, asset management or broader financial services environment.

• Graduate-level candidates with strong technical projects and internships will be considered.

Qualifications

• At a minimum, a degree in applied mathematics, statistics, quantitative risk management, financial mathematics, data science, computer science, pure science, business science, engineering or other technical or subject matter related field.

Additional Qualifications/Experience (Preferred, not a requirement)

• Demonstrable experience in data extraction, cleansing, transformation, and reporting at scale. Additional experience in IT, data warehousing, business analysis, or risk/finance reporting will be advantageous.

• Recognised certifications in SQL, Alteryx, Tableau or Python, data science or data analytics are advantageous.

Competencies Required

• Self-awareness

• Strong work ethics

• Intellectual curiosity

• Eagerness to learn

• Attention to detail

• Communication skills

• Ability to work independently and as part of the team

• Flexibility

• Patience

• Perseverance

• Ability to treat others with respect

• Initiative

• Ownership and pride in own work

 

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Skills

Action Planning, Analytics Software, Budget Management, Business Intelligence (BI) Analysis, Computer Literacy, Data Analysis, Data Compilation, Data Controls, Data Interpretations, Data Modeling, Evaluating Information, Numerical Aptitude, Report Review, Solution Analysis, Statistical Analysis Techniques

Competencies

Business Insight

Cultivates Innovation

Manages Complexity

Optimizes Work Processes

Situational Adaptability

Strategic Mindset

Education

Bachelors Degree (B): Applied Mathematics (Required)

Closing Date

07 August 2026 , 23:59

The appointment will be made from the designated group in line with the Employment Equity Plan of Old Mutual South Africa and the specific business unit in question.

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