Senior Manager Data Analytics MTN Vacancy
Senior Manager Data Analytics MTN Vacancy
Senior Manager Data Analytics MTN Vacancy
MTNVAC-207
Senior Manager Data Analytics
Roodepoort, ZA
Group Strategy
Group Strategy
Level 3H
Mission/ Core purpose of the Job
The Senior Manager Data Analytics role is responsible for the leading the architecture and development of advanced analytics use cases, modelling complex business problems, discovering insights and identifying business opportunities using statistical, algorithmic, Machine Learning/Mining and visualization techniques. In addition to advanced analytic skills, this person needs to be proficient at integrating and preparing large, varied training and test datasets, optimization of 3rd-party models, leading the design and rollout of analytical toolsets, and supporting MTN OpCo teams with their analytical capability building and design thinking..
The Senior Manager will be a creative thinker and propose innovative ways to look at problems. The role works closely with clients, data stewards, project/program managers, and other IT teams to turn data into critical information and knowledge that can be used to make sound organizational decisions or otherwise operationalized in real-time to deliver bottom line business results.
These professionals will need a combination of business focus, strong analytical and problem-solving skills and deep programming expertise to be able to quickly cycle hypothesis through the discovery phase of the project.
The role will validate findings using an experimental and iterative approach. The Data Scientist will need to present findings to the business by exposing their assumptions and validation work in a way that can be easily understood by their business counterparts
Context (Global influences, environmental / industry demands, organisational mission etc.)
ExCo has mandated the establishment of a Business Intelligence Competency Centre to evolve BI practices to better serve MTN. The vision of the BICC is to create a permanent core BI organization in Group (BICC), staffed with highly skilled full-time professionals from across the enterprise. This unit will work with Tech BI and BICC teams in OpCos to leverage Group’s BI capabilities and technology in support of the strategic initiatives of the business. In doing so, the BICC aims to achieve the following:
- Standardise BI across MTN
- Enable each OpCo to provide BI competency suited to their needs
- Agility and efficiency in all BI-related activities
- Suitability and relevance of MTN-developed BI solutions, in Group and OpCos
Key Performance Areas: Core, essential responsibilities / outputs of the position (KPA’s)
- Models and frames business scenarios that are meaningful and which impact on critical business processes and/or decisions, across descriptive, predictive, and prescriptive analysis.
- The ability to come up with solutions to defined business problems by leveraging pattern detection over potentially large datasets.
- Identifies what data is available and relevant, including internal and external data sources, leveraging new data collection processes such as smart meters and geo-location information or social media, ability to work with structured/unstructured/semi structured data.
- Collaborates with subject matter experts to select the relevant sources of information & translates the business requirements into a data mining project. Adept at breaking down a project into its constituent phases
- Proficiency in statistical analysis, quantitative analytics, forecasting/predictive analytics, multivariate testing, and optimization algorithms.
- Utilizes patterns and variations in the volume, speed and other characteristics of data supporting the initiative across a range of data formats (e.g., images, text, clickstream or metering data)
- Makes strategic recommendations on data collection, integration and retention requirements incorporating business requirements and knowledge of best practices.
- Develops content for and educates the organization both from IT and the business perspectives on new approaches, such as testing hypotheses and statistical validation of results. Helps the organization understand the principles and the math behind the process to drive organizational buy-in
- Defines the validity of the information, how long the information is meaningful, and what other information it is related to.
- High proficiency in machine learning techniques and algorithms, such as k-NN, Naive Bayes, SVM, Decision Forests, etc.
- Experience with common data science toolkits, such as SAS, R, SPSS, etc Strong proficiency in at least one of these is required
- Experience with data visualization tools, such as Power BI, Tableau, etc.
- Proficiency in using query languages such as SQL, Hive, Pig Experience with NoSQL databases, such as MongoDB, Cassandra, HBase
- Proficient communication skills & the ability to take complex outputs and present back to business owners
Job Requirements
Education:
- 4 years Bachelor’s degree in mathematics, statistics, computer science or related field
- Post-graduate degree in Data Analytics, engineering, or related technical area of study is strongly preferred
Experience:
- 4 – 8 years of relevant work experience in a global / multinational business environment (understanding of emerging markets advantageous)
- Minimum of 3 years of data analytics experience as a Data Scientist or Data Architect
- Deep knowledge and experience in data analytics tools (SQL, Python, SAS, etc.)
- Deep knowledge and experience in data reporting/visualization tools (Power BI, Tableau, etc.)
- Understanding of the Telecom domain and its main data sources is highly advantageous
Competencies:
- Logical Modelling
- Telecommunications Industry
- Influencing others
- Information processing
- Problem solving
- Risk management
- Analytics, Big Data
- Communication Skills (Verbal, Written)
- Analytical
- Continuous improvement
- Data interpretation
- Judgment
- Reporting
- Managing technology and commercial personnel
- The data scientist must be highly skilled in the design, development, and validation of descriptive, predictive, prescriptive, and applied Analytics
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