A kiosk is an informal convenience store selling everyday household items. Known locally by their vernacular names e.g. Duka in Kenya, Spaza in South Africa, Kantemba in Zambia. kiosk-type retail outlets are the cornerstone of African retail, accounting for over 60% of all retail trade flows. Despite their importance, kiosk-type retail outlets face significant challenges, including high cost of stock and unreliable delivery.

Role Profile

The Lead Data Scientist will be responsible for discovering insights from vast amounts of complex data sets and building models to support the business objectives and decision-making. The role holder will provide new insights into the business and utilize advanced statistical analysis, data mining, and data visualization techniques, to create solutions that enable enhanced business performance.

He/she will work as an expert in data manipulation, visualization, building and optimizing classifiers using machine learning and deep-learning-based techniques. The role has no direct reports currently, but the job holder should have the potential of managing a team.

Key Responsibilities:

• Project Identification, Execution and Management: Work with stakeholders throughout the organization to identify opportunities for leveraging company data to drive business solutions. Work with the Larger Business Intelligence team in identifying and integrating datasets that can be leveraged for further analytics and work with the Strategy & Data team members to strategize and execute the development of data products. Lead project execution to ensure that data project(s) scope is understood, completed on time and according to service delivery specifications. Conceive, plan, and prioritize data projects in alignment with organizational goals. Translate unstructured problems into well-defined machine learning projects or research.
• Trends and Patterns: Objectively analyze data for trends and patterns, propose analytic solutions and ideas to stakeholders, and implement improvements as needed to operationalize systems and processes. Mine and analyze data from company databases for improvement and optimization of strategic initiatives and operational processes. Execute analytical experiments methodically to help solve business problems and make an impact on strategic initiatives and operational processes.
• Data Preparation and Modelling: Assess the effectiveness and accuracy of new data sources and data-gathering techniques. Undertake cleaning and pre-processing of structured and unstructured data to build and enhance products, processes and systems. e.g. optimization; Smart pricing; Churn prediction analysis; Demand prediction and Market event forecasting. Use diagnostic, predictive, and prescriptive modelling to improve customer experiences and optimize revenue generation as well as other business outcomes. Develop and utilize algorithms and models to mine big data stores, perform data and error analysis to improve models, and clean and validate data for uniformity and accuracy.
• Stakeholder Management:Establish strong relationships with all stakeholders across the business and ensure work is prioritized according to business needs and opportunities. Collaboratively engage with executive leaders while leading business problem-solving using a broad spectrum of data science tools, packages and visualization techniques.
• Data Analysis: Lead the standardization of methods and algorithms used across the business to analyze data. Provide forward-thinking recommendations to the business by building an in-depth understanding of the problem domain and available business data assets, especially those pertaining to strategic initiatives and value-based programs. Undertake ad-hoc data requests to meet changing business needs.
• Leadership: Develop and lead a team of highly motivated and effective Data scientists in the near future. Manage team performance and create a high-performance culture within the team. Have frequent one-on-one sessions and performance dialogues to ensure delivery of the team’s mandate.
• Documentation: Develop up-to-date documentation of data science projects.

Requirements

Minimum Qualifications & Desired Skills:

• A degree in Statistics, Mathematics, Computer Science (technology), or another quantitative field;
• Relevant professional certificates;
• Minimum of 3 years’ experience in manipulating data sets and building statistical models;
• Programming Skills – knowledge of and experience with Statistical programming languages like R, Python, SQL, Java, Scala, C++, or JavaScript;
• Experience working with and creating data architectures;
• Experience with different machine learning techniques and algorithms (clustering, regression, simulation, scenario analysis, modeling, decision tree learning, artificial neural networks, k-Nearest Neighbors, Naive Bayes, SVM, etc.) and their real-world application;
• Experience in statistical and data mining techniques (text mining, GLM/Regression, Random Forest, social network analysis, Boosting, Trees, etc.);
• Experience visualizing and presenting data for use by stakeholders using Looker Studio or any other visualization tool;
• Statistics – Good applied statistical skills, including knowledge of statistical tests, distributions, regression, maximum likelihood estimators, etc. Proficiency in statistics is essential for data-driven companies;
• Excellent written and verbal communication skills for coordinating across teams and communicating findings to technical and non-technical teams;
• Desire to learn and master new technology skills and techniques;
• Strong problem-solving skills and aptitude;
• Ability to lead a team of Data Scientists;

Competencies & Key Skills:

• Data analytics;
• Analytical Thinking;
• Problem Solving Skills;
• Strong Project Management Skills;
• Stakeholder Management;
• Detail Orientation.