How Analytics changed Scouting in Soccer

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An interesting video that’s a great reminder on how Analytics is a game-changer when applied correctly. The video shared above how small clubs uses analytics to compete with big clubs and continue to not only stay relevant but grow in the process.

Similar analogy can be drawn for startups (or early-mid stage products inside big companies) where they can use Analytics to compete with incumbents in the market.

Let me know what you think. What’s your favorite analogy to help explain why analytics is useful to your org?

[Career Advice] What are the downsides of working as a data scientist in Silicon Valley?

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There are unique challenges to tech roles in Silicon Valley like housing costs & commute times but enough opportunity that it can make up for it if you prepare well. But this isn’t unique to data scientists so parking common challenges aside, here’s what I think is downside of working as a data scientist in silicon valley.

You see, every company follows the curve to reach an Analytics Maturity where after which a data scientist can start adding enormous value. I call it 3W curve.

What -> Why -> What’s Next.

What stage is a company in early analytics maturity stage where they are answering what questions. E.g. what are my sales for 2018? Here a Business Intelligence and Data engineer could help.

Why stage is a company in mid analytics stage where they are asking why questions. E.g. why did our sales go up in Q3 of 2018 compared to Q2 of 2018? Here a business analyst or product analyst can help.

After these two stages, company reaches the third stage where they ask what’s next questions. E.g. What is going to my top product growth area for next quarter? This is something that a data scientist could help with.

Now, having said that, Silicon valley has a lot of companies that are in early to mid stages and are better suited for Data engineers, Business intelligence engineers and Business/Product Analysts but they end up recruiting for “Data Scientists” (since it’s the sexiest term for all things data these days!) — this creates a mismatch in reality and expectation. The data scientist is expected to work on “advanced” analytics topics for a company where the culture and tooling is “basic”. This is a recipe for failure.

This delta is expectation vs reality is the biggest downside of working as a data scientist in silicon valley. To bridge this gap, hiring managers need to think through what their needs are and hire according to the needs (instead of hype) and the candidates should ask probing questions during interview process to judge the analytics maturity of the company to make sure it’s a great fit for them.

Also, I am not saying this delta doesn’t exist in other cities, it’s just that during my time in silicon valley, I noticed it more than I did in other cities. Silicon valley is a leader in tech so if this is fixed here then I expect other cities to follow the path.

originally answered on quora: https://www.quora.com/What-are-the-downsides-of-working-as-a-data-scientist-in-Silicon-Valley/answer/Paras-Doshi#