Designing Data Collection Tools 

• Designing and developing data collection tools, ensuring their quality and effectiveness. 

• Conducting quality checks on data collection tools to maintain high-quality standards. 

• Recommending and implementing improved tools and methods to enhance data collection processes, ensuring data integrity and quality are upheld.

Data Collection and Sourcing of High-Quality Data

• Collecting high-quality data to support ongoing projects across the organization by utilizing web scraping techniques and APIs to ensure accurate and reliable data for various initiatives across the organization.

Proposal Support 

• Incorporating data elements into proposals, enhancing them with a data-driven perspective. 

• Integrating statistical, mathematical, and machine learning approaches into proposals and projects across various business units. 

• Consulting on and contributing to projects that require intensive statistical or mathematical analysis.

Data Mugging and Analysis 

• Cleaning and preparing data for multiple projects across all business units within the organization. 

• Preparing analysis reports for various projects and presenting insights to relevant stakeholders. 

• Incorporating feedback from stakeholders into the analysis reports to enhance accuracy and relevance. 

• Integrating AI tools effectively into analysis projects to optimize results. 

• Conducting data analysis for initiatives across different business units and interpreting data to provide actionable insights that support informed decision-making.

Standard Operating Procedures

• Collaborate in developing data standard operating procedures for different tasks. 

• Develop supporting documents on maintaining and enhancing data integrity and security for all data handlers within the organization 

Designing and Implementing Business Analysis Tools

• Developing interactive dashboards tailored to the needs of various stakeholders across business units and clients, enabling data-driven decision-making and enhancing overall business performance.

Publishing Data Articles 

• Collaborating on creating articles covering diverse topics across various African countries, positioning DesignLab Research as a thought leader in multiple industries across the continent. 

• Proposing and developing publishing ideas in areas such as machine learning, data science, healthcare, and finance, among others, to highlight industry trends and innovations.

Innovation and Research

• Initiating and developing innovative projects and ideas for internal exploration and potential commercialization. 

• Leveraging AI and other resources to automate recurrent tasks and projects within the research team 

• Supporting and advancing ongoing innovative projects, including SCI Trade, Remote Sensing and other emerging machine learning and data science technologies, to drive growth and industry leadership.

Education & Qualifications:

• Bachelor’s degree in Data Science, Computer Science (with a focus on Machine Learning/AI), Statistics, Mathematics, Business Analytics, or a related discipline emphasizing analytical skills. Advanced degree holders (Master's or Ph.D.) are encouraged to apply.

Key Skills & Experience:

• Statistical Analysis and Data Mining - Strong foundation and understanding in statistical methods, data mining for extracting insights from structured and unstructured datasets.

• Proficiency in Data Analysis Tools and Techniques - Experience with data analysis techniques and software(‘s) (e.g., Excel, SQL, R, Python with libraries like, Tidyverse, Lubridate,  Pandas and NumPy ..etc) critical for data manipulation, cleaning, and analysis.

• Experience with Data Visualization and Business Intelligence(BI) Tools - Familiarity with data visualization tools (e.g., Tableau, Power BI, RStudio, Google Data Studio) to create clear and insightful reports and dashboards for stakeholders.

• Data Collection and Quality Assurance - Familiarity with data collection methodologies, web scraping, API usage, and implementing data quality standards.

• Research Experience - Prior involvement in conducting data-driven research, including data collection, analysis, and publishing findings. Contributing to research proposals is a plus.

• Data Standards -  Knowledge of developing data standards and ensuring data integrity and quality assurance throughout the data lifecycle.

• Machine Learning and  Artificial Intelligence - A grasp of machine learning algorithms and AI techniques, along with their practical applications in data-driven projects, is a plus.