Manager - Data Scientist.Group Strategy and Transformation

We at MTN are a purpose and value-led organization. At MTN, we believe that understanding our people’s needs and aspirations is key to creating experiences that delight you at work, every day. We are committed to fostering an environment where every member of our Y’ello Family is heard, understood and empowered to live an inspired life. Our values keep us grounded and moving in the right direction. Most importantly, they keep us honest. It is not something we claim to be. It is in our DNA.

As an organisation, we consider it our mission to create an exciting and rewarding place to work, where our people can be themselves, thrive in positivity and ignite their full potential. A workplace that boosts creativity and innovation, improves productivity, and ultimately drives meaningful results. A workplace that is built on relationships and achieving a purpose that is bigger than us. This is what we want you to experience with us!

Our commitments go beyond an organisational promise. It is in our leadership and managerial ethos to meaningfully partner with our employees, customers and stakeholders with a vision to realise our shared goals.
Live Y’ello 
•   Lead with Care 
•   Can-do with Integrity 
•   Collaborate with Agility 
•   Serve with Respect 
•   Act with Inclusion 

-   Models and frames business scenarios that are meaningful and which impact on critical business processes and/or decisions
-   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, the type of data (e.g., images, text, clickstream or metering data) in predictive analysis
-   Makes strategic recommendations on data collection, integration and retention requirements incorporating business requirements and knowledge of best practices.
-   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.
-   Excellent understanding of 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 Excellence in at least one of these is highly desirable
-   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

Education:
•   4 years Bachelor’s degree in mathematics, statistics, engineering or computer science or related field
•   Post-graduate degree in a related analytics/engineering field is a plus

Experience:
•   Minimum of 3 - 5 years of relevant work experience as a Data Scientist or Data Analyst, with experience building complex analytical models leveraging structured and/or unstructured data
•   Deep knowledge and experience in data analytics tools (SQL, Python, SAS, etc.)
•   Deep knowledge and experience in data reporting/visualisation tools (Power BI, Tableau, etc.)
Competencies:
-   Logical Modelling 
-   Telecommunications Industry
-   Influencing others
-   Information processing
-   Problem solving
-   Risk management
-   Analytics, Big Data
-   Communication Skills (Verbal, Written) 
-   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