From July to December 2024, I worked as a student intern at the Department of Climate Change and Meteorological Services in Blantyre. It was my first real exposure to how climate data is produced, processed, and communicated at a national level. A few things stood out.

The Scale of the Data

DCCMS operates a network of over 120 weather stations across Malawi, comprising 21 manned and over 100 automated stations. Every week, data from all of them needs to be quality-checked, processed, and turned into something useful. I was part of that process, analysing weekly and monthly records for rainfall, temperature, and humidity using different methods.

The volume alone was a useful lesson. Working with national datasets is different from working with downloaded API data in a personal project. The messiness is real, the stakes are higher, and the process has to be reliable, not just functional.

Translating Data for Different Audiences

One of my main responsibilities was contributing to national climate bulletins distributed to government ministries and agricultural agencies. The same underlying data had to be communicated differently depending on who was reading it.

A ministry of agriculture needs to know what rainfall totals mean for planting windows. A disaster risk office needs to know what temperature anomalies mean for drought probability. Neither audience wants to read about statistical methods. They want actionable information.

That translation work, turning technical findings into formats that support decisions, is something I underestimated before I started. It is harder than it sounds and more important than most data scientists give it credit for.

Working With Government Stakeholders

I participated in over eight stakeholder meetings with government ministries, NGOs, and development partners during the internship. Sitting in those rooms gave me a clearer picture of how climate data actually flows into policy, and where it gets stuck.

The gaps are rarely technical. The data usually exists. The challenge is institutional, communication, and trust between data producers and decision makers.

Why I Am Going Back

In March 2026, I return to DCCMS as a graduate intern. The experience was formative enough that going back felt like an obvious choice. This time, I have a clearer sense of what I want to get out of it and what I want to contribute.

The internship shaped how I think about climate data science, a career I am working toward, not just as an analytical discipline but as a public service. I am looking forward to contributing meaningfully to Malawi's Climate space.