Junior data scientists are told the problems they need to solve 👶
Senior data scientists find the problems that need to be solved 😎
Here is how they do it ↓↓↓
Senior data scientists find the problems that need to be solved 😎
Here is how they do it ↓↓↓
#Example: Imagine today is your first day at Spotify.
You are now part of the data science team responsible to maximize paid subscriptions and drive revenue.
The key question is:
"How can you increase subscription revenues with data science?" 🤔
You are now part of the data science team responsible to maximize paid subscriptions and drive revenue.
The key question is:
"How can you increase subscription revenues with data science?" 🤔
#1 Junior data scientist's idea 💡
Spotify is famous for its smart recommendations, and you completed a course on recommender systems with advanced Deep Learning techniques.
So you think:
"Let's build a better recommendation engine and increase subscriptions"
Wait a second...
Spotify is famous for its smart recommendations, and you completed a course on recommender systems with advanced Deep Learning techniques.
So you think:
"Let's build a better recommendation engine and increase subscriptions"
Wait a second...
... before you start typing any code:
→ Are you sure this is the best problem to focus on?
Many great data scientists have worked on this problem in the company before.
→ What are the chances you build a better model?
Honestly, pretty slim.
→ Are you sure this is the best problem to focus on?
Many great data scientists have worked on this problem in the company before.
→ What are the chances you build a better model?
Honestly, pretty slim.
You will fail because you focused too much on the solution (recommender systems) and not the actual problem (subscription revenue).
You have great technical knowledge, but you chose the wrong problem.
This is what a senior data scientist would do instead ↓
You have great technical knowledge, but you chose the wrong problem.
This is what a senior data scientist would do instead ↓
#2: Senior data scientist's idea 💡💡💡
Let's take a user-centric approach, and ask the question:
"Why does a user decide to start a paid subscription?"
Two are the factors behind her decision:
→ Quality: How good is the app? 👌
→ Price: How much does it cost? 💸
Let's take a user-centric approach, and ask the question:
"Why does a user decide to start a paid subscription?"
Two are the factors behind her decision:
→ Quality: How good is the app? 👌
→ Price: How much does it cost? 💸
Improving the quality of the app is doable, but HARD.
Instead, experimenting with the optimal pricing point is much easier.
So, what about identifying a segment with historically low conversion, and running an A/B test with different price discounts?
Instead, experimenting with the optimal pricing point is much easier.
So, what about identifying a segment with historically low conversion, and running an A/B test with different price discounts?
It is a solution with low technical complexity and higher chances of succeeding.
Which means a better problem to work on.
And hence, higher chances of success 🧠
Which means a better problem to work on.
And hence, higher chances of success 🧠
In conclusion,
→ Focus on finding the right problem to solve. This is the most critical part of the whole project (aka pick your battles wisely).
→ First the WHAT (to solve), then the HOW (to solve it). Not the other way around.
→ Focus on finding the right problem to solve. This is the most critical part of the whole project (aka pick your battles wisely).
→ First the WHAT (to solve), then the HOW (to solve it). Not the other way around.
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freelance-data-science.carrd.co
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