How to run science projects

In this article, I will outline my mental model for running a science project. Specifically, I’m referring to data or applied science projects, drawing from my experience of over 9 years at AWS and Amazon. You might argue that in agile environments like startups or smaller companies, the approach could differ, but aside from an additional layer of hierarchy, I don’t anticipate significant deviations.

The most crucial question to ask at the start of a project is whether the business problem is well-defined. A well-defined problem could be something like: what is the impact of a sales promotion? Or, what is the value of adding a new feature? However, as a scientist, you’re more likely to encounter vague business problems — if any are presented at all. For instance, you might be introduced to a business organisation and asked to figure out how you can make an impact on profitability.

Early in my career at AWS, I was introduced to a sales leader who wanted help with data science. We had a few meetings where I tried to identify the business problems they aimed to solve. To my surprise, they didn’t have any clear issues in mind and simply hoped I would work some “magic.” In hindsight, I should have proposed concrete projects focused on improving profitability, rather than an optimization project. It took me a few years to realize that a rapidly growing sales organization is primarily concerned with increasing profit by selling more, not through optimization efforts like churn analysis.

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