MTN Nigeria is part of the MTN Group, Africa\'s leading cellular telecommunications company. On May 16, 2001, MTN became the first GSM network to make a call following the globally lauded Nigerian GSM auction conducted by the Nigerian Communications Commission earlier in the year. Thereafter the company launched full commercial operations beginning with Lagos, Abuja and Port Harcourt. MTN paid $285m for one of four GSM licenses in Nigeria in January 2001. To date, in excess of US$1.8 billion has been invested building mobile telecommunications infrastructure in Nigeria. Since launch in August 2001, MTN has steadily deployed its services across Nigeria. It now provides services in 223 cities and towns, more than 10,000 villages and communities and a growing number of highways across the country, spanning the 36 states of the Nigeria and the Federal Capital Territory, Abuja. Many of these villages and communities are being connected to the world of telecommunications for the first time ever. The company\'s digital microwave transmission backbone, the 3,400 Kilometre Y\'elloBahn was commissioned by President Olusegun Obasanjo in January 2003 and is reputed to be the most extensive digital microwave transmission infrastructure in all of Africa. The Y\'elloBahn has significantly helped to enhance call quality on MTN network.
Mission:
To lead and drive the development and implementation of machine learning solutions for the consumer business unit and play a crucial role in driving business value by leveraging customer data to personalize experiences, predict churn, and optimize marketing campaigns.
Description
- Understanding business objectives and developing models that help to achieve them, along with metrics to track their progress.
- Develop and implement machine learning models for tasks such as churn prediction, customer segmentation, targeted marketing, fraud detection, and network optimization.
- Ensure the quality and fairness of machine learning models throughout the development and deployment lifecycle.
- Monitor and track the performance of machine learning models and measure their impact on business goals.
- Present findings and recommendations to senior management in a clear and concise manner.
- Stay up-to-date on the latest advancements in machine learning and share your knowledge with the team and broader organization.
- Ensure very deep market understanding: regular macro-economic review of trends and shifts that can affect the business and overall market demand.
- Interpret the customer base marketing strategy to identify, plan, and implement/build the analytical capabilities required to deliver the CVM base management strategy.
- Ensure timely base management reporting.
- Drive the ROI of base management activity by ensuring the provision and continuous improvement of actionable insights, analyses, CVM reports, and dashboards.
Education:
- First degree in Computer Science, Engineering, Statistics, Applied Mathematics, Economics, or a related discipline.
- Industry certification(s) and/or post-graduate or professional qualification(s) in a related field (an added advantage).
- Fluent in English
Experience:
6 - 13 years’ experience which includes:
- 4 years’ experience in the telecoms industry, with at least 2 years in a supervisory role.
- Expert understanding of programming languages such as SQL, Python, or R.
- Proven experience in developing and deploying machine learning models for real-world applications.
- Strong leadership skills with the ability to motivate and inspire a team.
- Strong understanding of statistical modeling, machine learning algorithms, and deep learning techniques.
- A track record of managing innovation, developing and applying creative solutions to business problems, anticipating situations and needs, and finding flexible answers to new situations.
- Experience in CVM methodology, principles, capabilities, and techniques.
- Excellent communication, collaboration, and presentation skills.
- Strong problem-solving and analytical skills.
- Experience with data pipelines and data preparation techniques.
- Expert knowledge of the competitive environment, consumer trends, and trade practices in the industry
- Experience in applying various quantitative techniques to address business problems.
- A self-starter who is self-motivated, disciplined, self-assured, performance driven and passionate about digital, AI, ML, and data and its role in transforming businesses.
- Experience reviewing code for analytics models and providing recommendations for performance improvement.
- Familiarity with cloud platforms (e.g., GCP, Azure)
Method of Application
Signup to view application details.
Signup Now
