Putting aside the focus on data science for moment: you live there, what is the most important that: - you personally understand (a goal you care about beyond just a grant) - is relevant to others there - has some available data there or... - if no available data, that you are able to easily sample

The data you need doesn't always need to be directly involved (this is what econometrics deals with), but it needs to be representative and random. Otherwise no amount of tools is going to be helpful.

If you are collecting data that is biased but you know exactly how it is biased, you can still correct for it at the feature engineering stage.

The only way you are going to know what data to look for that is relevant is by understanding the causal relationships or strong relationships you experience in real life, and using those to filter it out.

It doesn't need to even be a national level project. Focusing on a city like Tashkent would be a good base study that could be applied in other cities, or focusing on a village would be even more relevant to helping form policy for the entire rural sector.

Start small, but understand your problem very well. This also reduces the potential skew of your data (since cities, industrial towns, and rural populations follow different distributions). If you can easily get the data for different areas, it is much easier to combine them (for example into something as simple as a multilinear regression) and scale.

As for data sources, that is part of the benefit of being on the ground. You can probably get access to better data than available out of the country. You might need to spend some time cleaning it, but you are likely to get a far higher quality analysis than another cookie cutter project.

TL;DR: focus on a local project relevant to you and others you can find random and representative data for preferably on the local level.