Data Scientist - 12 Months Contract at Auditor-General of South Africa

Strategic Function

• Contributes to the strategy, vision, and governance of the business intelligence capability.
• Support the long-term strategy-level decisions using customer science techniques.
• Support the implementation of the centre Balance Score Card (BSC) project and initiatives.

Product Management

• Collaborate with cross-functional teams, leveraging successful initiatives, findings, and analytics techniques to benefit stakeholder decision-making.
• Using machine learning algorithms and techniques to build predictive models and make data-driven decisions.
• Collaborate with the senior data scientist and lead data scientist to build data pipelines using workflow orchestration tools.
• Schedule extracts and collaborate with senior scientist to draw data from APIs for analysis.
• Implement and train machine learning (ML), predictive analytics, data mining and artificial intelligence (AI) models to perform predictions and forecast behaviour and transactions, via the use of data, to enable the business to be proactive in decision making.
• Undertake the processing of structured and unstructured data to utilise in data science activities.
• Support the data analysis of large information to discover trends and patterns and provide feedback to the business.
• Highlight potential value in data science and articulate recommendations to best deliver business goals.
• Provide support in the implementation of configurable analytical models within the department.
• Support business functions through feasibility analysis and modelling, root cause analysis, decision support modelling, supply chain design modelling and analysis.
• Develop processes and tools to monitor and analyse model performance and data accuracy.
• Provide support to senior data scientist and lead data scientist to ensure the most optimised technical process is implemented across the business.
• Use statistical software to analyse, identify and assess data attributes to develop recommendations.
• Consolidate and analyse a variety of data in various formats across all business areas and propose mechanisms to make the business more efficient.
• Process, cleanse and assess the effectiveness of new data sources and data-gathering techniques to enhance analytics and business intelligence.
• Support in the delivery of data science and related projects on time, with the desired functionality, at the defined quality level in a sustainable way.
• Implementation of the Hackathon events
• Research, benchmark, design, implement and validate algorithms/models to analyse diverse sources of data to achieve targeted outcomes and provide recommendations to senior data scientist and lead data scientist.
• Identify, review, and promote creative ideas and approaches to enhance the continuous improvement department.
• Ensure optimal utilisation of technology solutions, keeping pace with innovations in data science capabilities.
• Conduct continued research in developing best in class statistical and machine learning models.
• Monitor, measure, support, and report on analytical results to ensure appropriate business recommendations and insights.
• Prepare statistics, dashboards, and information for management to derive business decisions.

Stakeholder Management

• Establish, build, and maintain collaborative working relationships with relevant internal stakeholders.
• Build and maintain positive and value-adding relationships with relevant internal/external stakeholders.
• Scan the environment to ensure a clear understanding of stakeholder needs.
• Proactively interact with stakeholders to determine their needs and deliver on them accordingly.
• Engage with both internal and external stakeholders to identify and evaluate performance barriers to continuously improve service delivery.
• Work in collaboration with senior data scientist and lead data scientist, business intelligence and data governance to ensure timeous delivery of the work.
• Establish and maintain relations with recognized professional bodies within their own professional sphere.
• Manage service level agreements (SLAs).

People Management

• Manage own and team performance to drive productivity.
• Motivate, coach and mentor staff to ensure maximum productivity and development of the staff to their full potential.Participate and / or take lead in the business unit transformation and culture plans.
• Review the work of peers assisting in the execution of work.
• Create development plans with staff to ensure alignment to latest technology trends.
• Provide support to the management team with regard to centre management and other people-related tasks.

Financial management and Operational Management

• Contribute to the compilation of centre budget, and manage project expenditure related to functional area.
• Ensure compliance with AGSA policies and procedures.
• Ensure compliance with internal processes and procedures.
• Manage supply chain processes within scope of work.

Other responsibilities (Applicable to All JD’s)

• Perform and/or manage other projects, tasks, and assignments not stipulated on the Job description as and when required.

Formal Education

• Minimum Bachelor’s degree in quantitative management (decision sciences) or Computer Science/Statistics/Applied Statistics/Applied Mathematics
• Honours will be advantageous

Preferable certifications:

• Data science
• SQL Server (MCSA: SQL Server)
• Microsoft Certification in data, data science or artificial intelligence

Experience/ Requirements

• Minimum 8 years of related experience working on data exploration and visualization tools.
• Experience with common data science toolkits such as SQL essential R, SQL, and sometimes Java or C++. Python
• Machine learning/ Artificial Intelligence and Statistical algorithm development.
• Experience in leading hackathons
• Experience and exposure to cloud solutions.
• Working with large volumes of structured and unstructured data and leveraging them to build artificial intelligence (AI)/machine learning (ML) and predictive modeling (PM) solutions through end-to-end automated data pipelines.
• Experience in developing data mining techniques and models across various algorithm types, understanding use case applicability.
• Machine learning operations (MLOps) for AI/ML model deployment and monitoring/enhancements for standalone solutions or as part of a larger product.