6months onsite Finance Data Scientist

ROLE DETAILS Reports to: Head: Centre IT Supports: Group Finance & Actuarial Function Location: Onsite (Parktown, Johannesburg) Employment type: Fixed term contract Level: Senior / Specialist ROLE PURPOSE To apply advanced analytics, statistical modelling and machine learning to finance and actuarial data in order to unlock measurable business value. The role turns business questions into analytical problems, builds and productionises models, and translates results into insight that shapes decision-making across the finance operating model, from forecasting and cost analytics to anomaly detection, automation and reporting intelligence. KEY RESPONSIBILITIES Problem Framing & Analysis - Engage finance and actuarial stakeholders to understand business challenges and frame them as analytical problems with clear success measures. - Perform exploratory data analysis to test feasibility, size the opportunity and pressure-test hypotheses before build. - Develop analytical business cases quantifying expected benefit, effort and risk. Model Development & Deployment - Design, build, validate and tune statistical and machine learning models: forecasting, classification, clustering, anomaly and outlier detection. - Productionise models through repeatable MLOps pipelines covering versioning, retraining, monitoring and drift detection. - Apply AI and generative AI techniques, including document intelligence and retrieval-augmented approaches, where they demonstrably outperform conventional methods. - Support the identification and delivery of RPA and intelligent automation opportunities in finance processes. Data Engineering & Preparation - Source, profile, cleanse and transform finance and actuarial data from ERP, sub-ledger, policy and third-party systems. - Build reusable feature sets and analytical data products on the group data platform. - Work with data engineering and architecture teams on pipeline design, data quality controls and lineage. Insight, Visualisation & Adoption - Build dashboards and visualisations that make model output actionable for finance users. - Present findings and recommendations to senior stakeholders in clear, non-technical language. - Train and support business users in interpreting and applying analytical output, and drive adoption of data-driven ways of working. Governance, Ethics & Model Risk - Document models, assumptions and limitations to satisfy model risk, audit and regulatory review. - Apply responsible AI, fairness, explainability and data privacy principles throughout the model lifecycle. - Contribute to the group's analytics standards, reusable code libraries and peer-review practices.

Requirements

ESSENTIAL EXPERIENCE & SKILLS - 6+ years in data science, advanced analytics or quantitative modelling, with 3+ years in insurance or financial services. - Strong understanding of finance and actuarial data, accounting principles and reporting standards. - Advanced proficiency in Python and/or R, and strong SQL skills for large-scale data manipulation. - Practical experience with machine learning libraries and frameworks such as scikit-learn, XGBoost, TensorFlow or PyTorch. - Solid grounding in statistics: regression and generalised linear models, time-series forecasting, hypothesis testing and experimental design. - Experience deploying models to production on cloud platforms (Azure ML, Databricks, AWS SageMaker or equivalent) with MLOps tooling. - Strong grasp of finance data flows, transformation, cleansing and visualisation, including Informatica or comparable ETL tooling. - Familiarity with Data Mesh, MDM and finance data lakes/warehouses. - Data visualisation and storytelling skills using Power BI, Tableau or equivalent. - Experience facilitating cross-functional workshops and presenting to senior stakeholders. - Proficiency in Git-based version control, Jira, and both Agile and Waterfall delivery methodologies. QUALIFICATIONS - Degree in Data Science, Statistics, Actuarial Science, Mathematics, Computer Science, Engineering or a related quantitative field; Honours or Master's preferred. - Industry-recognised certification in data science, machine learning or a cloud data platform is advantageous. DESIRABLE ADDITIONAL EXPERIENCE - Exposure to IFRS 17, reserving, pricing or capital modelling. - Familiarity with actuarial business capabilities, processes and IT architecture. - Knowledge of South African financial and insurance regulations and POPIA. - Experience mentoring junior data scientists and analysts. HOW TO APPLY Applications must be submitted via the Centrax Digital careers portal: https://careers.centraxdigital.com/jobs/Careers. Only shortlisted candidates will be contacted.

Benefits

Exposure and growth

• Production ML work embedded directly in a Group Finance & Actuarial function, not a generic analytics team, giving exposure to reporting standards, model risk and audit scrutiny that most data science roles don't touch
  
• Hands-on MLOps across a modern cloud stack (Azure ML, Databricks or SageMaker), moving models from notebook to production pipeline with versioning and drift monitoring
  
• Direct access to senior stakeholders through workshop facilitation and non-technical presentation of findings, which builds commercial and communication credibility alongside technical depth
  
• Actuarial-adjacent exposure (IFRS 17, reserving, pricing, capital modelling) is a genuine specialisation few data scientists get, and is highly transferable within insurance and financial services