diving into data science too quickly can distract from more pressing challenges facing the business on the path to scale. (View Highlight)
One rule of thumb to lean on is to start thinking about a hire when you reach 1,000 monthly users for at least six months. This establishes a stable user base with enough data for a data scientist to parse usage patterns and identify trends. Another good benchmark to keep an eye out for is that when your company hits the 50+ employees mark, where more specific functions are being built out (like finance), it’s likely time to consider a full-time data scientist. (View Highlight)
If a company’s data has grown to the point that qualitative assessments or basic Excel analyses no longer inform business decisions and accurately monitor business health, it may be time to bring in a data scientist. (View Highlight)
Even if you have enough data, it doesn’t necessarily mean you need to have a data science team. Data scientists help your company discover unknowns and patterns and validate your hypotheses — (View Highlight)
If you’ve already done a bunch of customer discovery work and developed a strong hypothesis about the product roadmap moving forward, you likely don’t need to spend the money on a data scientist to cosign your decisions. (View Highlight)
you might be better off having an external consultant or a contractor who can help guide your existing engineering and product orgs to look at the data by themselves. These external folks might also give you a taste of how it looks to properly work with data before you commit to building out a team. (View Highlight)
if your engineers or product managers have a solid data background and know how to write queries, you might consider starting with a contractor or a consultant. Or if you’re looking to get started with one specific project that requires data scientist expertise, but don’t anticipate any other immediate needs in the near future, that might be another indicator that you’re better off starting with external help. (View Highlight)
in the early days, they’ll spend a lot of time communicating their logging needs with engineers, battling with inefficient data tools and getting the right data for analysis. (View Highlight)
The first data scientist is always critical because they will build the foundation of your data model, define the role of data science within your organization, conduct interviews to decide who will be joining the early team and shape the culture of your data science org. (View Highlight)
the key elements that distinguish a good data scientist from a mediocre one are often not technical skills, but their storytelling and thought leadership abilities. (View Highlight)
I am a strong advocate of hiring a hybrid-type data scientist as your first data science hire — either a traditional data scientist with a strong ETL background or a data engineer who knows some foundational data analytics techniques. (View Highlight)
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