Kristal Joi Wise
Kristal Joi Wise is an innovative leader with a passion for leveraging data science and business strategy to drive transformation in organizations. As the Chief Transformation, Sales, and HR Officer at her current company in Ghana, Kristal plays a pivotal role in optimizing operations and fostering organizational growth through data-driven decision-making and agile methodologies. With certifications in Scrum, Business Analysis, and Neuro-linguistic Programming (NLP), Kristal excels in guiding her team toward achieving business objectives in dynamic environments.
Her recent work focuses on harnessing machine learning and data science to tackle real-world challenges, such as food security in Africa. Kristal is passionate about how technology can be applied to improve agricultural practices and strengthen local economies, particularly in areas most affected by climate change. She is currently expanding her knowledge and experience in predictive analytics, with the goal of using these tools to support agricultural innovation and economic development in Africa.
As a single mother living in Ghana, Kristal is also a role model for women and young girls aspiring to pursue careers in STEM fields. Her leadership journey, combined with her commitment to continuous learning and professional development, allows her to inspire others and create opportunities for women to break into the tech and business sectors.
As a first-time speaker at PyData Global, Kristal is excited to share her insights on how open-source data tools and machine learning can be applied to real-world problems like agricultural yield prediction and business optimization. She hopes to engage with the PyData community and contribute to the growing body of knowledge on using data science for social good.
Sessions
Abstract:
As the climate changes, farmers in Africa are facing enormous challenges, from unpredictable rainfall to shifting growing seasons. In this session, I will share how we can use machine learning (ML) models, built on open-source platforms like TensorFlow and Google Earth Engine, to predict crop yields for key staples such as maize and cassava. By looking at case studies from Kenya, Ghana, and Malawi, I'll show how ML is helping farmers decide when to plant, manage resources more efficiently, and reduce climate risks. I’ll also talk about practical tools—like community hubs, radio broadcasts, and SMS alerts—that ensure even non-literate farmers can use these insights. Expect to walk away with actionable ideas on how to implement these techniques in your own work on food security.