Data Scientist

Job Purpose Statement

• The Data Scientist also plays a key role in the management of:
• Build econometric and statistical models for various problems inclusive of projections, classification, clustering, pattern analysis, sampling and simulations.
• Build the foundation of state-of-the-art scientific and technical capabilities within the Data Science department in order to support several planned and ongoing data analytics projects.
• Provide forward-thinking recommendations to the business by building in-depth understanding of the problem domain and available business data assets, especially those pertaining to strategic initiatives and value-based programs
• Execute ad-hoc data mining and exploratory statistics tasks on large datasets related to the business 'strategies.
• Generate actionable insights applying advanced statistical techniques, for example, predictive statistical models, segmentation analysis, customer profiling, analysis, survey design, and data mining
• Collaborate with senior data scientists to communicate obstacles and findings to relevant stakeholders in an effort to improve decision-making and drive business performance.

Key Accountabilities (Duties and Responsibilities)

• Data Science and Innovation - 60% Design, develop, and deploy advanced statistical, machine learning, and AI models to solve complex business problems, ensuring they are robust, scalable, and maintainable, and drive innovative solutions that enhance decision-making processes. Lead initiatives to explore and implement cutting-edge data science methodologies and technologies, staying current with industry trends and researching new techniques to apply innovative approaches to improve products, services, and operations. Collaborate with data engineering teams and data quality to improve data collection, storage, and processing infrastructure to support advanced analytics, implementing best practices for data quality, data governance, and efficient data workflows to enable innovative data science projects. Continuously monitor, evaluate, and optimize the performance of deployed models, ensuring they meet business objectives and deliver actionable insights, and make iterative improvements to enhance their accuracy, efficiency, and impact.
• Customer - 20% - Collaborate and communicate with stakeholders, including Marketing, Retail, Corporate and customer service/experience teams, to understand customer needs and pain points, and translate data insights into actionable strategies that enhance the customer experience and drive customer loyalty.
• Internal Business Process - 10% - 100% adherence to policies, procedures and statutory guidelines.    Audit, Compliance and Risk Rating = Satisfactory
• Learning and Growth - 10% Engage in ongoing learning and professional development by attending workshops, conferences, and online courses to enhance personal expertise and contribute to the team's capabilities. Mentor junior data scientists and analysts by providing guidance, support, and feedback, helping them to develop their skills and grow professionally. Up to date with NCBA mandatory Academy courses.

Job Specifications

• Academic:  Bachelor’s degree in Statistics, Mathematics, Computer Science, Machine Learning, Economics, or any other related quantitative field. Working experience of the equivalent is also acceptable for this position.  Master's in data science/Math or any Quantitative discipline is an added advantage
• Professional:  Big Data or Data Science certification from recognized institutions. Cloud and/or AI certifications from recognized institutions

 Desired work experience:

• At least 5 years working experience working with data analysis/data science and business outcomes research within a fast-paced and complex business setting, preferably working as a data scientist.
• Experience working in probability and statistics, time-series analysis, or econometrics as well as experience in the use of machine learning methods, for example, linear regression, decision tress, and so forth.
• Experience as well as in depth knowledge of the Python programming language, SAS Enterprise Miner and substantial knowledge of big data platforms such as Aster and Hadoop.
• Experience in developing, deploying and monitoring of models