Each employee contributes significantly to the institution, and the assigned responsibilities should not be limited. Therefore, this position description is designed to outline primary duties, qualifications, and job scope but does not limit the employee or the organisation to just the work identified. We expect each employee to offer his or her services wherever and whenever necessary to ensure the success of the services we provide to our internal and external stakeholders.
Duties and Responsibilities
-Identify and collect relevant data from various sources (databases, APIs, web scraping, etc.). -Clean, transform, and pre-process data to ensure data quality and consistency. This includes handling missing values, outliers, and inconsistencies. -Perform exploratory data analysis (EDA) to understand data distributions,1 identify patterns, and generate hypotheses. -Develop and implement data pipelines for efficient data ingestion and processing. -Apply statistical techniques and machine learning algorithms to analyse data and extract meaningful insights. -Develop and evaluate predictive models to forecast future outcomes or classify data. -Build and optimise machine learning models using various techniques (e.g., regression, classification, clustering). -Select appropriate features and develop feature engineering strategies to improve model performance. -Conduct A/B testing and other statistical experiments to validate hypotheses and evaluate model effectiveness.
Qualifications and Experience
Bachelor's Degree: A strong foundation in a quantitative field is essential. Qualifications in one of the following: Computer Science Statistics Mathematics Physics Engineering (especially related to data or computation) Economics A Master of Science in Data Analytics is an added advantage.
Skills • Proficiency in Data Analytics tool such as Power BI and Microsoft Dynamics • Well versed with programming languages like Python, R, and SQL for manipulation and querying
