Stop shooting in the dark 🌑
Start building data science products that SOLVE business problems in the real world 🏗️💰
And this is how you do it ↓
Start building data science products that SOLVE business problems in the real world 🏗️💰
And this is how you do it ↓
As a data scientist, you are the "interface" between 2 teams:
→ The data engineers 👷🏽
→ The business people 👩💼
And your role is to build data products that transform raw data into high-quality business decisions.
→ The data engineers 👷🏽
→ The business people 👩💼
And your role is to build data products that transform raw data into high-quality business decisions.
The more you talk to both worlds, the more effective your work will be.
However, each of these 2 teams speaks a "slightly different language".
However, each of these 2 teams speaks a "slightly different language".
Business people, like Product Leads 👩💼, are focused on setting and hitting clear business outcomes.
You need to talk to them regularly, to make sure you solve the right problem for the company.
For example, your Product Leads might say:
👩💼: "We want to increase user retention"
You need to talk to them regularly, to make sure you solve the right problem for the company.
For example, your Product Leads might say:
👩💼: "We want to increase user retention"
You know WHAT you need to solve.
Let's now move on to HOW you can solve it.
For that, you need relevant, high-quality data. Without high-quality data, you cannot measure retention, and hence you cannot measure your progress.
Without high-quality data, you will fail.
Let's now move on to HOW you can solve it.
For that, you need relevant, high-quality data. Without high-quality data, you cannot measure retention, and hence you cannot measure your progress.
Without high-quality data, you will fail.
Data engineers 👷🏽 take care of the infrastructure necessary to make high-quality data accessible to you.
So they are your best ally at this stage.
Back-and-forth conversations between you and the data engineer are a MUST if you want to succeed as a data scientist.
So they are your best ally at this stage.
Back-and-forth conversations between you and the data engineer are a MUST if you want to succeed as a data scientist.
Good conversations between data engineers and scientists result in concrete actions. For example,
→ let's add Facebook third-party data to enrich user profiles
→ let's remove duplicate entries in the transactions table
→ let's make the data available to frontend dashboards
→ let's add Facebook third-party data to enrich user profiles
→ let's remove duplicate entries in the transactions table
→ let's make the data available to frontend dashboards
Once you have high-quality data and a business problem, you are ready to do your data science magic.
3 ways of attacking this problem are ↓
3 ways of attacking this problem are ↓
Idea #1 → Build a user retention dashboard 📊
The Product Lead can use it to break down this metric by relevant user properties (e.g. geo, age).
Dashboards are a great way to keep the conversation flowing between product people and you.
I recommend you start with this.
The Product Lead can use it to break down this metric by relevant user properties (e.g. geo, age).
Dashboards are a great way to keep the conversation flowing between product people and you.
I recommend you start with this.
Idea #2 → Explore the data yourself to find the low-hanging fruit (aka quick wins) 🔍
For example, you might find that most Facebook campaigns bring low-retention users, so you ping the marketing team to stop them and shift the budget to another campaign.
Quick and easy win.
For example, you might find that most Facebook campaigns bring low-retention users, so you ping the marketing team to stop them and shift the budget to another campaign.
Quick and easy win.
Idea #3 → Predict churn events with ML 🔮
Sometimes you need to bring out the big guns and use Machine Learning.
You can build a churn-prediction model, to flag customers who are likely to churn, so the marketing team can engage with these users before they leave.
Boom.
Sometimes you need to bring out the big guns and use Machine Learning.
You can build a churn-prediction model, to flag customers who are likely to churn, so the marketing team can engage with these users before they leave.
Boom.
In conclusion, this is the data scientist's mantra that brings joy and productivity ↓
Wanna become a freelance data scientist?
Join my e-mail list and get my eBook "How to become a freelance data scientist", for FREE ↓
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Join my e-mail list and get my eBook "How to become a freelance data scientist", for FREE ↓
freelance-data-science.carrd.co
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