Core purpose of the Job:
To determine insights and develop statistical models from large quantities of structured and unstructured data to shape or meet specific business needs and goals.
Job Specifications/ Minimum & Preferred Requirements
Education / Business Degree
Minimum of 3-year tertiary degree in (STEM); Engineering, Computer Science, Mathematics, Statistics, Biological Sciences or related quantitative field
Fluent in English and Honours degree in relevant field
Work Experience
Minimum of 5 years’ experience in a data science environment, with experience in model development, interpretation and deployment.
Practical experience with and theoretical understanding of algorithms for classification, regression, clustering, and anomaly detection
Management experience beneficial.
Experience working in a medium to large organisation
Worked across diverse cultures and geographies advantages
Training
Data science and advanced analytics.
Data base modelling and data warehousing principles (SQL)
Big data tools (Spark, Hive, etc)
Strong Programming skills (R, SAS, Python, C++, Java, etc…)
Computer software development
Data Visualisation (Power BI, Tableau etc.)
Solution automation
Public GIT and Kaggle profiles a major plus
Azure, GCP or other cloud solutions
Industry / Certifications
Relevant certification / accreditation
Any other specifications
Attitude of Innovation, inventiveness and thought leadership
MS Office (Word, Excel, and Outlook)
Understanding of the MTN framework or Telecommunications industry
Worked across diverse cultures and geographies
Knowledge of modern Big Data platforms and Open Source Solutions
Competencies
Global Talent Standards
Sensitivity: Public
• Supportive People Manager
• Relationship Builder
Hands – Results Focused (40)
• Results Achiever
• Operationally Astute
Key Performance Areas
Measures (KPIs for job)
Key Job Responsibilities
Tactical Implementation
− Align all operational activities undertaken in the department to the BRIGHT strategy objectives
− Drive best practice, iterative improvement and innovation
− Provide recommendations to various business units and influences future plans based on insights
Data Modelling
− Research and develop custom data models and algorithms to apply to data sets
− Design data science approach, applying tried-and-true techniques or developing custom algorithms as needed by the business problem
−
Lead multiple analytic project teams by serving as the senior technical thought leader for project teams; working with various business units to translate project goals into data analytic model
− Build predictive and data mining models with high dimensional and dynamic data and conduct analysis through the project life cycle
− Effectively communicate and interpret model results for business
− LLM Model prototypes and incorporating OpenAI prototypes in solutions
Data Analysis
− Utilise Big Data analytics and advanced data mining techniques to analyse data and extract meaningful business insights and the story behind the pattern.
− Code, test and maintain analytical software tools
− Investigate adversarial trends, identify behavior patterns, and respond with agile logic change
− Perform analytical tasks such as text mining and sentiment analysis using Big Data techniques and algorithms.
− Analyse data from company databases to drive optimisation and improvement of MTN SA’s data quality and compliance
− Provide on-going tracking and monitoring of performance of decision systems and statistical models
− Communicate results of analysis to business partners/stakeholders and executives.
Continuous Improvement
− Develop usage and access control policies and systems in collaboration with the SM: Data Governance (Stewardship)
− Partner with the other data scientists in continuous improvement processes impacting data quality in the context of the specific use case
− Recommend ongoing improvements to methods and algorithms that lead to findings, including new information
Sensitivity: Public
− Engage with business teams to find opportunities, understand requirements, and translate those requirements into technical solutions
Collaboration / Coordination
− Collaborate with various stakeholders throughout the organisation to identify opportunities for leveraging company data to drive business solutions
− Collaborate with other functions across the business to ensure effectiveness of analysis conducted
− Collaborate with other functions to drive and where applicable enhance the utilisation of models, algorithms and software that help solve business problems
− Collaborate with data engineers and platform architects to implement robust production real-time and batch decisioning solution
Reporting
− Review and submit reports addressing periodic activity, budget compliance, feedback and key departmental results to facilitate decision making
− Track and report on model performance deployed in production.
− Prepare reports on operational performance and detailed reports on specific projects when required
− Communicate to business units the various predictions and findings through effective data visualisations and reports