What sorts of exits should I be looking for within data analytics if I'm more of a business person that a technical person? I feel like I'm technically competent, but my claim to fame is moreso domain expertise and knowing what to do rather than being a stats/programming mastermind. FWIW, my skills include:

-SQL
-Python (pandas, numpy mostly)
-Excel
-Alteryx
-PowerBI (shockingly, I'm worse at this than Python)

Any advice on what to do post-consulting/what skills I should focus on or develop?

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Business Analyst, BI Solutions Product Manager/Owner, Technical PM, Engagement Manager, and eventually Practice Lead.

The business takes sales. And sales takes speaking it plainly in such a way it can be understood. It also takes keeping business in dev/pipeline. Your skills become more obsolete, but your interoperability, adaptability, and knowledge base to sell are key.

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Data Analyst, Data Scientist, Business intelligence engineer, product manager (analytics)

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Similar question. I was thinking exciting to industry in a manger or director of analytics role would be appropriate?

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That was a couple of years ago. I was a Director and exited as an MD in Financial Services. Global Head of Analytics (I basically got my ex client promoted and took his role).

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I am in a similar boat - specially at a SM level. I will never be as good as a developer with SQL, so I focus more on my project management, cross functional team coordination, and client management skills.

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I have/had a similar profile to you, and found Data Product Management to be my calling. The other related field is Analytics Translator, but that’s more like being an Engagement Manager but on analytics projects - personally I was much more interested in developing scalable solutions (and the work-life balance that comes more easily than with project-based work), but the overlap between the two is heavy. You do zero (or almost zero) technical work, but any tech know-how helps tremendously both in building rapport with your team, as well as making sure what’s being promised and delivered is sensible.

Demand for both is growing as orgs realise that hiring a bunch of data scientists but not investing in product/translation/owners is a recipe for not delivering things of value. In my recent interviews competition was v limited, way way less than if you were applying for a DS role (even if DS is probably harder and requires more YOE for senior roles)

Happy to chat further if you wanna DM me :)

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