Built In: What's the ideal ratio of junior-to-senior data scientists?
Sometimes a ratio just works. The so-called golden ratio, or divine proportion, repeats again and again in nature — popping up everywhere from flower petals to pine cone spirals. In tech teams, we haven’t found anything quite so cosmically aligned, but the search for balance remains an evergreen concern. Consider the question: What’s the best ratio of junior-to-senior contributors?
It’s an oft-discussed topic in software engineering, but less so in data science. There is, of course, best-practice advice around team construction for data teams, forever evolving though it may be. But that tends to revolve around considerations like the number of data engineers per data scientist. As dbt Labs CEO and founder Tristan Handy noted earlier this year, the junior-to-senior consideration is more of an open issue for data teams.
“It’s a bit of a balancing act.”
Still, it’s worth considering seriously. Lyndsey Padden, vice president of data science at 84.51°, a Kroger-affiliated marketing analytics firm, told Built In that failing to maintain a proper balance can either stifle professional development or diminish the overall quality of work.
“Having too many senior data scientists creates a top-heavy organization and doesn't necessarily give people the right accountabilities or opportunity to stretch into talent management — or even technical skill development areas,” said Padden.
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