Thread by @paulabartabajo_

There is one skill every professional data scientist must have, that no online course talks about 🤔

🧵↓

Every aspiring data scientist I talk to thinks their job starts when someone else gives them

→ a dataset, and
→ a clearly defined metric to optimize for, e.g. accuracy

They are wrong.

Things are slightly more complex in the real world.

In the real world, data science projects start from a business problem.

They are born to move a key business metric (KPI).

The data scientist's job is to translate a business problem into the *right* data science problem.

Then solve it.

To translate a business problem into *the right* data science problem you do 2 things:

1 → ask questions
2 → explore the data to find clues.

There is nothing more frustrating than building a great data science solution, to the wrong business problem.

Imagine you are a data scientist 🧑🏽‍🔬 at Uber.

And your product lead tells you:

👩‍💼: "We want to decrease user churn by 5% this quarter"

There are different reasons why a user would stop using Uber.

For example:

→ "Lyft is offering better prices for that geo" (pricing problem)
→ "Car waiting times are too long" (supply problem)
→ "The Android version of the app is very slow" (client-app performance problem)

You build this list ↑ by asking the right questions to the rest of the team.

You need to understand the user's experience using the app, from HER point of view.

Typically there is no single reason behind churn, but a combination of a few of these.

The question is: which one should you focus on?

This is when you pull out your great data science skills and EXPLORE THE DATA 🔎

You explore the data to understand how plausible each of the above explanations is.

The output from this analysis is a single hypothesis you should consider further.

Depending on the hypothesis, you will solve the data science problem differently.

For example...

#Example 1: "Lyft is offering better prices for that geo" (pricing problem)

Solution: Detect the segment of users who are likely to churn (possibly using an ML Model) and send personalized discounts via push notifications.

#Example 2: "Car waiting times are too long" (supply problem)

Solution: Identify the location and time where supply is too low, and offer a price incentive for divers to cover these slots.

#Example 3: "The Android version of the app is very slow" (client-app performance problem)

Solution: Go to the frontend devs, show them the breakdown of use churn by app version, and convince them they should release a new version of the app with better performance.

In conclusion,

→ Translating business problems into *the right" data science problem is what separates a senior from a junior data scientist.

→ Ask the right questions, list possible solutions, and explore the data to narrow down the list to one.

→ Solve this one problem.

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