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Analytics Roles & Skill Sets

In the world of data and analytics, the myth of the unicorn analyst still exists - the analyst who can log the data and build a data warehouse, run the latest machine learning models, and translate it clearly into business recommendations with domain expertise. In reality, these tasks require very different skill sets, from engineering to statistics to domain expertise (and that’s before even considering the soft skills around influence, communication, partnership, and more). Data and analytics professionals tend to have particular skill sets that set them up for excellence in a certain area of the field. Recognizing this can set an individual up well to differentiate and claim their data strengths and a company up well to hire for the talent they need.

The world of data has three particular skill sets that cover a wide array of roles - domain expertise, statistics (including machine learning and AI), and engineering.

  • A domain expert deeply understands the context of which they are providing analytics and data support, be it product, marketing, sales, operations, etc. They know how to connect the dots from insights to action and what to recommend from the findings.

  • A statistical expert has a deep understanding of models and methodology to apply advanced techniques to drawing sense and insights of large data sets. AI experts would be part of this group as well.

  • A data engineer has the expertise to turn pixels and clicks into datasets that an analyst can then interpret.  They know best practices for efficient extraction and manipulation of the data.  

Venn diagram with 3 circles for domain expertise, statistics, and engineering skills for analytics role types

While anyone working in the data and analytics industry can benefit from having some knowledge across these skill areas, most specialize in (and are most passionate about) a subset of these expertises.  

To make things even more complicated - different companies use the same word to mean different things. A data scientist at Meta includes product analyst type roles, whereas a data scientist at Intuit implies a strength of statistics and advanced models. Some companies expect a data scientist to have a production level engineering skill set, some don’t. Looking into the details of the job description can help disentangle what skills are actually needed.  

So who am I as an analyst?  I am a subject domain expertise - I lean hard into the product or marketing strategy of the org I’m partnering with and how we can use the data and insights produced by my team to fuel the strategy and overall impact. I have background in the statistics side through my Masters and have a solid understanding of A/B experimentation, a critical tool for product impact and understanding. I have the least strength in engineering - you can find people with much more expertise to set up your data infrastructure! And, that’s fine - I’ve found my strengths and passions, leaned in hard to developing them, and own my piece of the analytics pie.

If you’re a company trying to figure out what data and analytics expertise you need, I can help.  If you’re an analyst determining and owning your strength with clarity and confidence, I can help.


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