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MTN Group Limited entered the telecommunications scene at the dawn of South Africa’s democracy, in 1994. In 1998, we began our expansion by acquiring licences in Rwanda, Uganda and Swaziland. Since then, we continued to grow, with a view of bringing world-class telecommunications and digital services to markets across Africa and the Middle East. Throug...
Mission/ Core purpose of the Job
The Manager: ML Operations / Decision Scientist is responsible to build and use analytical tools to create data-driven insights and recommendations for the BankTech suite of products & services. The Manager will leverage AI and ML platforms to provide in-depth analysis to inform strategic decisions that drive revenue growth.
The Manager is responsible for operational & commercial performance improvements through data driven recommendations and craft presentations to convey analysis to influence strategic decisions. The manager will also provide subject matter expertise in maintaining & optimizing platform capabilities to enable applications of Data Engineering and Automation, AI, Machine Learning and its use cases while such as Machine Learning, AI and Data Process Automation, , Intelligent Mining etc. and maintaining the operational efficiency of the platform and its allocated budget.
Further the Manager will operationally execute on analytical, statistical, and programming interpretation of data to support decision making and drive business results.
Key Performance Areas: Core, essential responsibilities / outputs of the position (KPA's)
Strategy Development and Implementation
- Provide reports and analysis to support and contribute to the development of the functional BankTech strategy in line with the overarching business goals
- Ensure accurate and timely reporting to enable the regular review of the functional strategy, roadmap and performance to ensure its alignment with the changing dynamics of the internal and external ecosystem
Operational Delivery
- Support and lead the design, develop and deployment of ML based platform for BankTech services
- Responsible for deployment the infrastructure components to enable use of the state-of-the-art techniques in Artificial Intelligence and Machine Learning
- Support and manage End-to-end machine learning model integration
- Design and implement MLOps processes across BankTech use cases
- Consistent improvement CI/CD applications for collaborations and enhancement to drive develop once and deploy to many mindset through container repositories
- Support, develop and design best practise data engineering and MLOps frameworks required to enable automated orchestrations that can be shared, reusable and collaborated upon in order to maintain and enhance the infrastructure required for consistent data management, decision science models monitoring and reporting across Group BankTech- Finco
- Manage extension of company’s data with third party sources of information when needed in line with the legal & regulatory governance frameworks & policy
- Ensure machine learning solutions are compliant with infrastructure, information security, privacy, banking regulations & regulatory requirements
- Security and virtual network peering of the scoring API in line with best practise
- Manage the processing, cleansing, and verifying the integrity of data used for analysis
- Perform ad-hoc analysis and presenting results in a clear manner
- Develop actionable insights to provide future based guidance to drive product performance and revenue
- Collaborate with Data Engineering team to build data pipelines for machine learning
- Work with Opco teams where applicable to for training & implementation projects
- Define, create, and maintain business metrics endorsed through security and audit compliance and governance
- Maintain budget allocation across the services and resources of the ML platforms
Governance
Operational, Tactical and Strategic Meetings
- Provide input in strategic meetings when required
- Provide inputs into the risk mitigation and controls
- Provide input into the preparation of proposal on change initiatives, policies and procedures
Escalations
- Escalate issues that will result in severe time, scope, productivity, and cost or resource impact
- Provide solutions to escalations that have multiple processes / functions impact on critical path of service delivery
Function Tactical
- Provide input into all projects initiated
- Provide input into establishing objectives, targets and budgets for the function as applicable
- Identify and document key risks, issues and dependencies and set mitigation actions
- Prepare documentation required for sign-off / making decisions regarding tactical changes
Performance
- Ensure execution in alignment with divisional strategy
- Continuous performance monitoring and adjust strategy and actions to deliver targets
Reporting
- Report on a periodic basis relating to progress made within the function and in accordance with the measurement metrics set by the organisation
- Report on an ad hoc basis on specific projects, as required
Job Requirements (Education, Experience and Competencies)
Education:
- Minimum of 4-year tertiary degree in Computer Science, Mathematics, Statistics, Data Science or related field
- Master’s Degree in a Financial, Commerce, Statistical or related field (preferred)
- MBA or Masters (advantageous)
- AI/ML professional certification (advantageous)
Experience:
- 5 or more years of relevant work experience as a ML Ops Engineer / Data Scientist
- At least 3 years’ experience within a non-traditional FinTech, Banking or Financial Services Sector
- Experience in deploying data science models in a machine learning platform role
- Experience in ML Ops within banking or financial services industry is advantageous
- Experience with MLOps platforms and deploying and monitoring machine learning solutions and applications
- Experience in building architectures around Machine Learning systems
- Proven experience with ML model management platforms, like MLFlow, wandb.ai, or Sagemaker or KubeFlow
- Proficiency in developing production micro-services and APIs
- Strong knowledge of containerization and orchestration technologies (Kubernetes, docker)
- Deep understanding of Cloud and on-premise deployment of Machine learning
- API experience and understanding of microservices and Kubernete insfrastructure
- Experience of working in an Agile/DevOps environment
- Proficiency in working with Python (Numpy, Scikit-learn, Pandas, Scipy, Matplotlib, Tensorflow, Keras, Seaborn, Bokeh), SAS or R / Scala for data clean up and advanced data analytics
- Working knowledge in Hadoop, Apache Spark and related Big Data technologies (MapReduce, PIG, HIVE)
- Demonstrated experience utilizing software tools to query and report data: SAS (Enterprise Guide and/or programming), tableau, Power BI,D3 VBA, SQL, and Business Objects
- Highly proficient in database management systems like Postgres, Oracle, Mongo, MSSQL
- Experience in ecommerce and electronic payment business is advantageous
- Experience working across global locations/ regions and have a grasp of political, social, infrastructure and integrity challenges
Competencies:
Functional Knowledge:
- Proficiency in working with AI/ML capabilities that leverages both cloud (Azure,GCP or AWS) and on-premise infrastructure.
- Machine learning frameworks such KubeFlow and MLFlow
- CI/CD orchestrations and repository
- Proficient in working with open-source languages such Python Jupyter Notebook, R / Spark – Scala and others to drive optimized data engineering and machine learning best practise frameworks
- Knowledge of AI/Machine Learning- Deep Learning and its applications
- Strong analytical skills with ability to automate reports that tells a story through visualization by leveraging standard enterprise BI tools like Power BI, Data Studio, Elastic Search-Kibana and many others
- Working knowledge in Hadoop, Apache Spark and related Big Data technologies and their applications in data engineering and MLOps pipelines
- Highly proficient in data warehouse and management for RDBMS and latest Big Data capability
- Ability to design, deploy and maintain machine learning and predictive model for different business use cases
- Data Engineering, Mining and analytics
- AI/ Machine learning for predictive modelling and other relevant use cases
- Payment, E-Commerce and digital platforms
- Understanding of FinTech, banking, microfinance and payment businesses
- Working knowledge in related Big Data technologies
Skills
- Digital marketing
- Analytics and Interpretation
- Strategic Thinking
- Organizational Agility
- Digital mind-set
- Dealing with ambiguity and complexity
- Decision Making
- Conflict Management
- Numerical
- Project Management
- People Management
- Executive Presentation
Behavioural Qualities
- Analytical
- Organised and methodical
- Operationally astute
- Proactive
- Detail-oriented
- Driven
- Results-oriented
- Team player
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Mission/ Core purpose of the Job
- The Data Engineer / Data Scientist is responsible for driving the analytical, statistical, and programming interpretation of data to support decision making and drive business results. The Data Scientist supports product, teams with insights gained from analysing company & customer data to provide business predictions, proposals, and recommendations to improve business outcomes
- The role of the Data Engineer / Data Scientist is to leverage internal and, where applicable, external datasets to build and evolve, using “best practice” methodologies and statistical techniques.
- The Data engineer/ Data Scientist is also accountable for the execution & building the next generation foundational BankTech data infrastructure and enable delivery of actionable insights to value generating outcomes
Key Performance Areas: Core, essential responsibilities / outputs of the position (KPA's)
Strategy Development and Implementation
- Provide reports and analysis to support and contribute to the development of the functional BankTech strategy in line with the overarching business goals
- Ensure accurate and timely reporting to enable the regular review of the functional strategy, roadmap and performance to ensure its alignment with the changing dynamics of the internal and external ecosystem
Operational Delivery
- Develop and manage data models in accordance to company risk and profit profile to ensure business and customer growth
- Plan, create, and maintain data architectures in line with business requirements
- Responsible for developing required infrastructure for optimal extraction, transformation and loading of data from various data sources
- Responsible to build analytical tools to utilize the data pipeline
- Develop and Manage data models & architecture for BankTech in the areas of machine learning and data sciences in building the various components of the semantics layer
- Extract, analyse, and interpret large data from a range of sources, using algorithmic, data mining, artificial intelligence, machine learning and statistical tools to drive optimisation and improvement of product development
- Manage the Data Architecture for full set of banking services that includes savings, loans, and insurance
- Develop advanced quantitative modules using a variety of programs/software to support predictive assessment
- Assess the effectiveness and accuracy of new data sources and data gathering techniques
- Develop, continuously improve and execute an efficient framework to identify, organize, structure and make available data attributes and information relevant for BankTech
- Produce intelligent, scalable, and automated solutions by leveraging Data Science skills
- Perform complex analyses, including optimization, text analytics, machine learning, social-science modeling, and statistical analysis, parametric and non-parametric statistical models and techniques
- Work with Technology teams on the development of new data capabilities & architecture to define requirements and priorities
- Manage the definition, development and evolution of Finco’s Digital Banking prediction capability, and the optimisation thereof
- Work with Opco teams where applicable to standardize Data Architecture sets and improve the data quality
Governance
Operational, Tactical and Strategic Meetings
- Provide input in strategic meetings when required
- Provide inputs into the risk mitigation and controls
- Provide input into the preparation of proposal on change initiatives, policies and procedures
Escalations
- Escalate issues that will result in severe time, scope, productivity, and cost or resource impact
- Provide solutions to escalations that have multiple processes / functions impact on critical path of service delivery
Function Tactical
- Provide input into all projects initiated
- Provide input into establishing objectives, targets and budgets for the function as applicable
- Identify and document key risks, issues and dependencies and set mitigation actions
- Prepare documentation required for sign-off / making decisions regarding tactical changes
Performance
- Ensure execution in alignment with divisional strategy
- Continuous performance monitoring and adjust strategy and actions to deliver targets
Reporting
- Report on a periodic basis relating to progress made within the function and in accordance with the measurement metrics set by the organisation
- Report on an ad hoc basis on specific projects, as required
Budgets
- Manage functional budgets in line with overall budget and business objectives
- Managing project initiative budgets in line with business objectives
- Support the reduction of cost of operations, in line with a least cost operating strategy stemming from the business drivers
Job Requirements (Education, Experience and Competencies)
Education:
- Minimum of 4-year tertiary degree in Computer Science, Mathematics, Statistics, Data Science or related field
- Master’s Degree in a Financial, Commerce, Statistical or related field (preferred)
- MBA or Masters (advantageous)
Experience:
- 5 or more years of relevant work experience as a Data Engineer/ Data Scientist
- At least 3 years’ experience within a non-traditional FinTech, Banking or Financial Services Sector
- Experience in Data Science and Data Analysis within banking, finance and/or telecommunications industry
- Experience in Data Engineering within banking or financial services industry
- Understanding of enterprise-scale systems and technologies used in data infrastructures
- Experience of working in an Agile/DevOps environment
- GitHub or GitLab experience for CI/CD
- Consistent improvement CI/CD applications for collaborations and enhancement to drive develop once and deploy to many mindset through container repositories
- Proficiency in working with Python and its relevant libraries SAS or R / Scala for data clean up and advanced data analytics
- Working knowledge in Hadoop, Apache Spark, and related Big Data technologies (MapReduce, PIG, HIVE)
- Demonstrated experience utilizing software tools to query and report data and being software agnostic
- Highly proficient in database management systems like Postgres, Oracle, Mongo, MSSQL
- Experience in data analysis and management, business performance management and/or reporting within the financial sector or banking industry
- Experience working in a medium to large organization
- Experience in ecommerce and electronic payment business is advantageous
- Experience working across global locations/ regions and have a grasp of political, social, infrastructure and integrity challenges
Competencies:
Functional Knowledge:
- Proficiency in working with data engineering capabilities that leverages both cloud (Azure,GCP or AWS) and on-premise infrastructure.
- CI/CD orchestrations and repository
- Proficient in working with open-source languages such Python Jupyter Notebook, R / Spark – Scala and others to drive optimized data engineering and machine learning best practise frameworks
- Strong analytical skills with ability to automate reports that tells a story through visualization by leveraging standard enterprise BI tools like Power BI, Data Studio, Elastic Search-Kibana and many others
- Working knowledge in Hadoop, Apache Spark and related Big Data technologies and their applications in data engineering and MLOps pipelines
- Highly proficient in data warehouse and management for RDBMS and latest Big Data capability
- Ability to design, deploy and maintain machine learning and predictive model for different business use cases
- Data Engineering, Mining and analytics
- AI/ Machine learning for predictive modelling and other relevant use cases
- Payment, E-Commerce and digital platforms
- Understanding of FinTech, banking, microfinance and payment businesses
- Working knowledge in related Big Data technologies
Skills
- Digital marketing
- Analytics and Interpretation
- Strategic Thinking
- Organizational Agility
- Digital mind-set
- Dealing with ambiguity and complexity
- Decision Making
- Conflict Management
- Numerical
- Project Management
- People Management
- Executive Presentation
Behavioural Qualities
- Analytical
- Organised and methodical
- Operationally astute
- Proactive
- Detail-oriented
- Driven
- Results-oriented
- Team player
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