I think there is so much potential in investing data science at Gitcoin.
This is informed by a few things
- Prior Experience. A few years ago, I was the Director of Engineering at a clean energy startup that set up a fairly robust data warehouse ETL/snowflake schema system to run advanced analytics on time series data. I’ve also been in a few different product oriented positions at various web2 startups that had mission critical ecommerce checkout flows with A/B testing, marketing emails to optimize those funnels. This one time, when i was CTO of an online dating site (a double sided marketplace, just like Gitcoin), I built a matching engine that matched users on 20 dimensions.
- Per gitcoin.co/results, Gitcoin has helped 66,712 funders reach an audience of 292,817 earners. Gitcoin has facilitated 1,740,075 complete transactions to 10,247 unique earners. Understanding the 4 years of data at Gitcoin, particularly the Grants Rounds data, gives me a hunch that there are interesting opportunities in understanding the data.
The objective of this thread is to start a conversation. What should the data science practice at Gitcoin look like?
Here are the data science opportunities matters I’m aware of at GitcoinDAO
- Product Analytics & Data Science
- Responsible for understanding how users use the platfrom.
- Marketing Analytics & Data Science
- Responsible for understanding how to drive more core actions (like Grants checkouts)
- Complex Systems Insights
- Responsible for guiding the QF matching engine with deep analytical insights (perhaps one day even simulating agent-based contributor behaviour)
- Responsible for publishing advanced analytics-based insights from our datasets. Heres an example of what this could look like.
- Fraud insights
- Responsible for (Joe, correct me if I’m wrong) surfacing fraud on the Gitcoin Grants network (whether sybil or collusion) and partnering with Governance to remediate in a legitimate way.
An assortment of tools are used in these practices at Gitcoin. Here are the ones I’m aware of:
- Etherscan
- Dune Analytics
- The Graph
- PostgresSQL
- Google Analytics
- Metabase
- Google spreadsheets
- Google presentations
- Acquia
- CADCAD
- Machine Learning Tools (not sure which ones)
I’d welcome corrections from any workstream leads on the above. The above is just my best approximation of the tools/roles as they currently stand int he DAO.
I’d be curious if people in the community would be interested in putting forward a proposal to the DAO to formalize a data science practice at GitcoinDAO (which currently resides in multiple different groups at varying levels of coordination)
I’d like to end on this questions:
- What should the data science practice at Gitcoin look like?
- If Data Science was an area of practice at Gitcoin, what would it look like?
- How could it span multiple workstreams or squads(teams) & cross-pollinate between them?
Disclaimer: This post is for informative purposes only and is not financial advice. This post reflects my personal views, not a decision or commitment by Gitcoin governance. Forward-looking items in it are targets, not commitments. The information in these posts is subject to change as we continue learning. This post may contain estimates, may contain errors, and is provided on a best-effort basis. DYOR, do not make any financial decisions based on these posts.






