Data Science Manager

 Position : Data Science Manager (visionary)

Location: Pretoria

Salary: Market related based on experience

Work setup: Hybrid – 2 days a week in office

Introduction

Our client is seeking a visionary Data Science Manager that will lead the Data function, bringing deep technical expertise and strategic oversight to ensure optimal data management, utilisation and advancement in data science. This role demands a highly skilled professional with extensive experience in data, AI-driven analytics, and predictive modelling.

The Data Manager will play a pivotal role in developing and scaling data science capabilities within the company, integrating AI and advanced data modelling to drive strategic decision-making and business innovation. In addition to leading the data team, the Data Manager will spearhead efforts in technical implementation, operational efficiency, and project execution, ensuring that all data solutions are seamlessly integrated into company initiatives.

The data team has a subject focus on geospatial data, and the Data Manager would be expected todevelop or have a knowledge base in this space.

Minimum Requirements

Qualifications & Experience:

• Bachelor's degree (Master's preferred) in Computer Science, Data Science or a related field.

• 8+ years of experience in data management, data science or a similar role, with at least 3 years in a leadership capacity.

• Background in AI model development, machine learning, and predictive analytics.

• Proficiency in programming languages (Python, R, SQL) for data processing and automation.

• Extensive experience in data cleaning and data mining principles.

• Strong background in data analysis, visualisation, and predictive modelling.

• Excellent communication skills, with the ability to present complex spatial data to non-technical stakeholders.

• Knowledge of Geographic Information Systems (GIS) and related spatial data would be

advantageous, including GIS software (QGIS, MapInfo, ArcGIS) and spatial databases (PostGIS, SQL Server, Oracle)

Roles & Responsibilities

Departmental Management Activities:

• Oversee the daily operations of the data team, ensuring efficiency, resource allocation, and continuous optimisation.

 
• Conduct regular standup meetings with the team to ensure alignment and to identify

obstacles.

• Preparation of management reports.

• Compile and submit Capex requests to enable monthly and quarterly data releases.

• Compile data usage reports.

Team Leadership & Development:

• Lead the data team, providing mentorship, development opportunities, and strategic

direction.

• Responsible for team performance management and ensuring team contributions are

recognised and aligned with organisational objectives.

• Foster a collaborative, results-driven culture focused on quality, innovation, and problem-

solving.

• Champion the integration of AI and machine learning within the data science function.

Data Science & AI Strategy:

• Develop a robust data science framework to support AI-driven decision-making and business

intelligence.

• Oversee the implementation of AI-powered data analytics and automation across the data science function.

• Lead research and development initiatives in AI modelling, machine learning, and predictive analytics, as this applies to the GIS space and other relevant data sets/models

Data Management & Strategy:

• Oversee the end-to-end management of data, including collection, processing, storage, and distribution.

• Responsible for monthly and quarterly data releases.

• Ensure that all datasets align with organisational needs and adhere to quality standards.

• Research and evaluate additional datasets to enhance the company's data science offerings.

• Manage data service providers to ensure compliance with contractual obligations and

technical standards.

Data Analysis & Problem-Solving:

• Oversee and contribute to complex data analysis tasks, including data modelling and the creation of detailed visualisations to inform decision-making.

• Oversee identification of shortcomings and data quality problems within internal and

external datasets.

• Oversee and contribute to technical team leads on data product development and project delivery.

Technology & Tools Implementation:

• Drive the adoption of best-in-class data science tools and technologies.

• Evaluate, implement, and maintain platforms, GIS software, and related tools in alignment with company needs and budget.

• Perform regular technology, data, and AI model quality reviews to drive cost optimisation.

• Provide technical recommendations and oversee creation of support documentation to enhance data science processes.

Client Engagement:

• Ensure high-quality results are delivered on time and within budget.

• Oversee all data deliveries to clients, ensuring contract obligations are met.

• Provide technical support to the Sales team.

Cross-Departmental Collaboration:

• Work closely with internal teams, including Product Development, Operations, IT and Sales to integrate data solutions that support broader business objectives.

• Gather requirements and develop project plans, including effort estimates.

• Serve as a primary point of contact for all data-related queries.

Specialised Data Solutions:

• Oversee and contribute to custom data workflows, automation, and analytics tailored to business needs. This may involve advanced data processing, 3D modelling or data

visualisations.

Business Intelligence Integration:

• Oversee and contribute to translation of data insights into actionable business intelligence by integrating data with internal analytics and reporting systems, such as business dashboards.

Compliance & Data Governance:

• Develop and enforce data governance policies for data, including security, privacy, metadata standards, and compliance with any relevant regulations.

• Work closely with stakeholders to understand risk assessment requirements and translate them into data solutions.

• Ensure data compliance with regulatory standards and internal quality benchmarks.

Data Modelling:

• Manage and maintain high-quality datasets used in risk modelling processes.

 
• Collaborate with analysts and developers to ensure accurate data integration into risk and other models.

• Oversee data validation, cleansing, and transformation procedures to support model

reliability.

• Support the development of dashboards and reporting tools to communicate risk insights.

• Lead periodic audits and reviews of data used in modelling to identify gaps or inconsistencies.

• Document data management workflows, risk data definitions, and metadata clearly for team-wide use.

• Design and contribute to the development of derived measures and data sets based on client requirements and company strategic objectives.