Data Scientist at Development Bank of Southern Africa (DBSA)

Job Description    

• The purpose of this role is to transform raw data into actionable insights that drive strategic decision-making and uncover patterns, forecast trends, and solve complex business problems. The role leverages statistical analysis and modelling, data engineering techniques, machine learning, cloud computing and Machine Learning Operations (MLOps) practices and big data technologies to generate actionable insights, improve ICT service delivery, and strengthen the advanced analytics capability and improve efficiency.

Key Responsibilities    
Advanced Data Analysis and Modelling

• Contribute to the development of data science strategies aligned with ICT and organisational objectives.
• Adhere and comply with data governance, security, and privacy frameworks.
• Establish standards for data quality, integrity, and lifecycle management.
• Develop and deploy predictive and prescriptive models, machine learning algorithms, and AI solutions , including Generative AI and Large Language Model (LLM)-based solutions where appropriate, to solve business challenges and improve operations.
• Deliver models that forecast outcomes or recommend optimal actions, enabling proactive and strategic decisionmaking.
• Conduct data analysis and implement data engineering techniques to identify trends, anomalies, and uncover hidden patterns and insights that inform business strategies, highlight risks, and reveal areas for growth or efficiency.
• Analyse structured and unstructured data to identify trends, risks, opportunities and translate into actionable insights for ICT optimisation and business decision-making.
• Validate model performance using appropriate metrics and refine as needed.

Data Management and Preparation

• Clean, transform, and prepare data for analysis and modelling. Produce high-quality datasets that are accurate, consistent, and ready for analysis, reducing errors and improving model performance.
• Collaborate with data engineers to ensure data pipelines meet analytical requirements. Establish seamless data flows that support scalable and efficient analytics, ensuring timely access to relevant data.
• Maintain documentation of data workflows and models. Enable transparency and repeatability in data science processes, supporting auditability, collaboration, and long-term sustainability of solutions.
• Apply MLOps practices – including version control, CI/CD pipelines, automated monitoring and retraining to manage models throughout their production lifecycle.

Insight Generation and Communication

• Translate complex data findings into clear, actionable business insights. Provide decision-makers with understandable and relevant interpretations of data that directly inform strategy and operations.
• Create compelling visualisations and narratives to support strategic decisions. Use dashboards, charts, and storytelling to make data accessible and persuasive, enhancing stakeholder engagement and buy-in.
• Present results to stakeholders and support data-driven decision-making. Facilitate informed discussions and decisions by clearly communicating findings and recommendations tailored to business needs.

Innovation and Research

• Contribute to the development of a data science knowledge base and best practices. Advance the organisation’s analytical maturity by sharing learnings, experimenting with new techniques, and promoting continuous improvement in data science methodologies.
• Research ways to enhance ICT infrastructure performance by applying data science methodologies.
• Explore emerging technologies in AI, big data, and cloud computing to strengthen ICT capabilities.
• Drive continuous improvement initiatives to enhance efficiency and reduce ICT operational risks.
• Benchmark data science practices against industry standards to ensure competitiveness.

Collaboration and Project Delivery

• Collaborate with ICT teams to integrate analytics solutions into enterprise systems.
• Support automation, digitalisation, and ICT portfolio management through data-driven approaches.
• Work closely with business units to understand analytical needs and define project objectives.
• Align data science initiatives with business goals, ensuring relevance and impact of analytical solutions.
• Partner with various business units and Operations teams to deliver cross-functional data solutions.
• Integrate data science into broader business projects, enhancing collaboration and accelerating delivery.
• Ensure timely delivery of high-quality data science outputs aligned with business priorities. Meet deadlines and quality standards, driving measurable value through data-driven solutions that support strategic objectives.

Key Measurements of Outputs

• Usability of predictive models with forecasting accuracy, delivered within agreed project timelines to support ICT and organisational decision-making.
• Percentage completeness and cleanliness of datasets, with documented validation processes ensuring accuracy, reliability, and compliance with governance standards.
• Accurate dashboards, reports, and visualisations that translate complex data into reports delivered within agreed timelines.
• Quality and accuracy of analytical reports or dashboards delivered.
• Impact of insights generated for business decision making and dashboard optimisation to deliver on business needs.
• Quality of data sets and insights generated from raw data.

Expertise & Technical Competencies    

Digital Acumen

• The ability to understand, contribute to, and drive the adoption of digital technologies and processes within an organisational context to improve efficiency, create new value, and transform operations.

Data Management

• Defines a data management plan and drives this through respective function.
• Defines policies for file storage.
• Develops a backup plan and schedule and backs up data accordingly.
• Ensures proper files are selected and backed up and stored off site and in proper storage conditions on site.
• Defines backup test procedures and recovery on a regular basis.
• Defines Cross checking procedures and policy.

Data Collection and Analysis

• Based on knowledge of the reasons behind the analysis, is able to define the most appropriate means of data collection.
• Is able to develop formats for data collection.
• Is able to define the most appropriate internal and external data/information sources.
• Identifies key facts in an array of data, recognises when pertinent facts are incorrect, missing, or require supplementation or verification.
• Breaks down data into component parts to understand the nature and relationship of the parts.
• Has a broad knowledge of statistical datahandling techniques.
• Can undertake more comprehensive analysis of data/information but is not required to draw conclusions.

Quality Management

• Ability to develop quality management plans and inspection protocols. Implements systems and processes to ensure quality is built into the design and planning stages and conducts audits and reviews to verify compliance with standards.

Reporting

• Prepares both standard and non-standard reports to time and quality standards.
• Collate and analyses readily available data for inclusion in a report.

Minimum Qualification

• A Bachelor’s Degree in Data Science, Statistics, Mathematics, Engineering, Computer Science, or a related field.

Minimum Experience

• A minimum of 8 years of hands-on experience in data science, ICT data optimisation or analytics roles.
• Experience working with large datasets and deploying models into production environments including on cloudbased platforms (AWS, Azure or Google Cloud Platform).
• Proven expertise in machine learning, statistical modelling, and big data technologies.
• Practical experience applying MLOps principles (model versioning, deployment automation, monitoring and retraining) to manage models in production.
• Proven track record in working with programme languages such as SQL, Python, R, Power BI, Tableau, or similar.
• Experience in statistical analysis, data modelling, and machine learning techniques.
• Knowledge of ICT governance frameworks, cybersecurity, and compliance standards.
• Familiarity with data governance frameworks, data quality management, and compliance standards.

Desirable Requirements

• Postgraduate qualification in Data Management or related fields.
• Certifications in machine learning, AI, or cloud-based data science platforms e.g., AWS Certified Machine Learning – Specialty, Microsoft Certified: Azure Data Scientist Associate, or Google Professional Machine Learning Engineer).
• Exposure to Generative AI, Large Language Models (LLMs) and Natural Language Processing (NLP) techniques and their practical business applications.
• Membership of a relevant professional body (e.g., South African Statistical Association or the Institute of Analytics).

Deadline:17th September,2026