Our mission

Toyin leads data strategy at Garnet Big Data, where she specializes in translating complex business problems into dashboards that executives actually use. Her superpower lies in making highly technical work entirely understandable to non-technical audiences, allowing business leaders to move quickly and act decisively on data.

Before founding Garnet, Toyin worked across various multinational operations, including Leadway, one of Nigeria's largest insurance groups, where she oversaw data strategy, team performance, and critical executive analytical systems. Notably, she architected the company's shift to a cloud-based ELT reporting stack on Microsoft Fabric and Azure Databricks, granting leadership immediate access to business-critical insights.

With a technical footprint spanning insurance, fintech, and education, she has successfully trained over 100+ developers in Python and analytics systems. She thrives in fast-paced, high-impact remote environments where business intelligence drives core strategic decisions.

Ruby brings deep expertise in machine learning, cloud architecture, and building production-grade data engineering infrastructures. Holding an academic background in Medicine and Biochemistry, she firmly believes in empowering clients with sustainable data infrastructure they can completely own outright rather than creating restrictive vendor-lock dependencies.

Before co-founding Garnet, Ruby spent over 6 years as an AI Engineer and Data Scientist, constructing advanced statistical frameworks and predictive platforms for healthcare, biomedical research, and demanding industrial applications. Her technical execution covers cloud-native ecosystems on AWS, GCP, and Azure capable of scaling to process millions of complex records.

She has successfully integrated advanced AI functionalities directly into traditional architectures for notable international entities like the UN, WHO, and IBM across telecom, e-commerce, and IoT sectors, consistently managing cross-functional technical teams under demanding production timelines.