Senior Data Scientist at Tezza Business Solutions Ltd

Data Science and Innovation

• 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

• 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
• 100% adherence to policies, procedures and statutory guidelines.

 Ideal Job Specifications

 Academic:

• Bachelor’s degree in Statistics, Mathematics, Computer Science, Economics, or any other related quantitative field. Working experience of the equivalent is also acceptable for this position.
• Masters 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:

• Minimum of 4 years of experience in data analysis, data science, or business outcome research within a fast-paced and complex business environment, ideally in the banking or financial services sector.
• Proven experience in building, deploying, and monitoring credit risk scorecards for lending to customers in a banking or financial services context.
• Solid background in probability and statistics, time-series analysis, or econometrics.
• Hands-on experience with machine learning techniques such as logistic/linear regression, decision trees, and other supervised learning models.
• Proficient in Python, R.
• Practical knowledge of big data platforms such as Aster, Hadoop, or other distributed data processing systems.
• Demonstrated ability to develop, deploy, and maintain predictive models, with attention to model monitoring, drift detection, and governance practices.