As the demand for Web3 solutions has exploded over the past year, so has the number of postings for specialized Web3 talent. There are now thousands of jobs posted every month by startups to more established Web3 companies. 

Web3-savvy data scientists are among the roles in high demand. Crypto exchanges, protocols and NFT platforms increasingly recognize that they need in-house data analytic, economic and business intelligence capabilities.    

But there is an issue. The majority of data scientists are schooled in the world of Web2. For example, the platform economy has grown significantly over the past decade, and this is where many job opportunities have been. Amazon alone now employs more than 250 PhD economists who specialize in all manner of data analysis across Amazon’s platforms.  

Some of these capabilities overlap with the needs of Web2 companies. For example, Web3 companies often need similar technical skills. They need staff with a strong working knowledge of big data processing technologies, such as Hadoop, Hive and Spark, and analytical coding packages like R and Python. The ability to write SQL queries and transform raw data into reports and dashboards is often desired. Experience with statistical hypothesis testing is also valued, such as with A|B experiments, which can help determine what product strategies resonate better with customers.   

Both Web2 and Web3 companies need data strategists who can collaborate effectively with various business functions, including finance, engineering, marketing and the senior executive team. They both need expertise in data security and data privacy. And at advanced levels, they need to be able to design data systems and data governance that reliably and consistently supports business strategy and planning.  

But there are also key differences. For example, a recent job posting by OpenSea stated they need data scientists who can: “Define data—help define, iterate and socialize key Web3 concepts using blockchain data (e.g., a unified definition of transactions that accounts for bundles, aggregators, etc.); build, maintain and optimize in-house Dune dashboards.” 

This would be a completely new terrain for most Web2 data analysts.   

Likewise, the platform Artie recently posted a position for a Web3 economist who could help the company, "Track, interpret, refine and communicate macroeconomic trends and their impact on the NFT market and Artie product." At present, the number of economists versed in NFT marketplaces is vanishingly small.   

There are many unique challenges given the newness of many Web3 products like NFT collectibles. Experience over the past year suggests that more than a few projects have done a poor job of forecasting demand. Some projects have underestimated demand causing drops to sell out too quickly and forgoing significant upside. Others have overestimated demand, minted collections too high and have not sold out. Many projects have also significantly underestimated the cost of acquiring customers who are not familiar with the space.

Web3 companies will often need their data analysts to have a firm understanding of the economics of tokens or what has become known as tokenomics. 

There is no doubt that many Web2 data scientists can make the transition to Web3. The question for the companies making these hires is time-to-productivity. Will it take three months? Five months? Or nine months for them to reach their peak capability? Training can accelerate this period, but Web3 analytics is so new that standardized courses are extremely scarce, and the quality of the offerings remains uncertain. 

It would also be a mistake to assume that training is only needed on the analyst side. The leadership team of Web3 companies can also benefit from a deeper understanding of how data analytics can benefit their business. This can lead to leadership teams asking better questions and defining job roles more effectively.  

There are a number of ways that Web3 companies can improve the prospect of landing just the right analytic talent.

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Tap emerging networks 

Communities are being established that focus specifically on blockchain data analytics. For example, Dune Analytics is building a community of “Dune Wizards” who openly publish their analytics work drawing from an array of blockchains. These communities can provide insight into emerging talent.

Set aside funds for training

Web3 companies should establish a specific training budget. While this is an added expense, it will improve both recruitment and retention. The funds can be used to help to transition a strong Web2 data analyst to the world of Web3. If they already have Web3 experience, there is always more to learn, especially as things are moving so fast. 

Improve recruitment

Ensure your recruiters and hiring managers write a strong compelling job posting. In the face of strong competition, you need to ensure they clearly articulate the role and career opportunity. Given the newness of these roles, recruiters may need additional support to ensure they are looking in the right places and applying the right questions in screening candidates.  

These steps require investment. However, the payoff can be significant. Good data analysts can make the difference between flying blind and hoping for success and data-driven decision-making that reduces business risk and improves the chances of sustained growth.

Peter C. Evans is the managing partner of the Platform Strategy Institute and the co-chair of MIT Platform Strategy Summit.


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