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Dec 29 β€’ 8 tweets β€’ 4 min read
Wanna become a freelance data scientist? 😎

5 tips to help you become one ↓
#Tip 1: Start small

Clients donΒ΄t look for an all-in-one data scientist, but someone who can solve their SPECIFIC problems.

Identify the things you are already an expert in, e.g.

β†’ Dashboarding with Tableau, or
β†’ ML for computer vision, or
β†’ Scrapping

Apply only for these.
#Tip 2: Build a Minimum Viable Portfolio

Clients want to see real work you have done in the past. They want to see solid proof you can deliver.

Build a small public/private portfolio that focuses on your strengths (from #Tip 1 above).
#Tip 3: Fish in several ponds

There are lots of freelance platforms nowadays, so don't put all the eggs in the same basket.

The 2 platforms I would start with:

β†’ Toptal: toptal.com/Bxdpg6/worlds-…
β†’ Brainstrust: app.usebraintrust.com/r/pau1/
#Tip 4: Write proposals like a pro

Go straight to the point. Focus on the problem from the first paragraph, without preambles and presentations that can only make her yawn.

Decrease cognitive load by using bullet points, and close the proposal with a call-to-action.
#Tip 5: Pricing

Hourly pricing is the best option if you correctly set your hourly rate.

Data science hourly rates fluctuate between 40 USD/hour and 150 USD/hour.

Never go below 40, you will be leaving money on the table.
Wanna become a top freelance data scientist?

Join my e-mail list and get my eBook "How to become a freelance data scientist". For free ↓

freelance-data-science.carrd.co
Wanna get more freelance career advice?

Follow me @paulabartabajo_ so you do not miss what's coming next.

Wanna help?
Like/Retweet the first tweet below to spread the wisdom ↓↓↓

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More from @paulabartabajo_

Pau Labarta Bajo Profile picture
Dec 27
4 strategies to build a better Machine Learning model.

🧡 ↓
Your model performance is the end result of combining 2 basic ingredients:

β†’ a dataset, and
β†’ an algorithm

If you wanna improve your model results, you need to improve either one of these 2 things.

Here are 4 ways in which you can improve them ↓
Strategy #1. Add more samples to the dataset

The more samples you feed to your algorithm, the higher the chances the algorithm picks up the existing patterns in the data.

If you work with a tabular dataset, this means you wanna have more rows in your data.
Read 8 tweets
Pau Labarta Bajo Profile picture
Dec 20
How to turn an ML notebook into a batch-prediction service

(using only Python and free MLOps tools)

πŸ§΅β†“
The starting point is this one Jupyter notebook where you:

1 - Loaded data from a CSV file
2 - Engineered features and targets
3 - Trained and validated an ML model.
4 - Generated predictions on the test set.

Let's turn this notebook into a batch-prediction service ↓
A batch-prediction service ingests raw data and outputs model predictions on a schedule (e.g. every 1 hour).

You can build one using this 3-pipeline architecture
- Feature pipeline πŸ“˜
- Training pipeline πŸ“™
- Batch inference pipeline πŸ“’

Let's go step by step...
Read 11 tweets
Pau Labarta Bajo Profile picture
Dec 15
"An ML model with better offline evaluation metrics is a better model in production."

But is it really? πŸ€”

Here are 4 steps to test if your ML model is better than the one running in production 🧠 ↓
A better offline metric does NOT mean a better model, because

β†’ An offline metric (e.g test ROC) is *just* a proxy for the actual business metric you care about (e.g money lost in fraudulent transactions)

β†’ The ML model is just a small bit of the whole ML system in production
So the question is:

"How do you bridge the gap between offline proxy metrics and real-world business metrics?" πŸ€”

Here are 4 methods to evaluate your ML model, from less to more robust ↓
Read 12 tweets
Pau Labarta Bajo Profile picture
Dec 13
You trained a great ML model inside a notebook, but it doesn't work in production.

Why? πŸ€”

Because your ML model is as good as the dataset you use to train it.

If your data has a bug, your model has a bug πŸ›

Look at the most common data bug and the best way to solve it 🧠 ↓
Let's say you work at Netflix, and you wanna build an ML model to predict which users will cancel their subscriptions.

You have plenty of historical *events* for each user, that you can use to engineer good model features.

And this is when things get interesting... Image
You discover that `event_4` ("user visits payment method page") has an 80% correlation with churn likelihood.

In other words, `event_4` is a great feature for your ML model.

So you add it to your training dataset.
And you train your ML model.
And you get 99% accuracy.

Boom.
Read 12 tweets
Pau Labarta Bajo Profile picture
Dec 1
Wanna become a professional data scientist? πŸ‘©πŸΎβ€πŸ”¬πŸ‘¨β€πŸ”¬

One that feels
- knowledgeable 🧠
- confident 😎
- and ready to charge well what she knows? πŸ’°

Here is what you should do (spoiler alert, it is hard, but worth it) ↓↓↓
The internet is flooded with Data Science/ML content:

β†’ blog posts
β†’ newsletters
β†’ Twitter threads
β†’ Arxiv papers
β†’ ...

And the thing is, reading all that is not gonna get you a job.

You need to get your hands dirty β›οΈπŸ‘·πŸΎβ€β™‚οΈπŸ‘·πŸ»β€β™€οΈ
Real learning in data science (like in life) happens when you

β†’ face a specific problem
β†’ struggle to solve it, and
β†’ eventually solve it.

I call this the "problem-struggle-solution cycle".

This is how you learn everything in life.
And data science is not an exception.
Read 14 tweets
Pau Labarta Bajo Profile picture
Nov 29
Training an ML model inside a Jupyter notebook is something every data scientist knows πŸ‹οΈ

But do you know how to create a real-world ML service that makes a difference for the company you work for? πŸ“ˆ

If the answer is NO, this thread is for you πŸ€—πŸ§΅β†“
So, what is the difference between model training and ML service? πŸ€”

An ML service is a sequence of processing and storage steps that takes in raw data and outputs predictions that are used by the business to make smarter decisions.

Model training is just one of those steps.
And while the model you've trained in Jupyter notebook IS important, you need to build the rest of the system to make it work.

How do you do that?

2 solutions ↓
Read 9 tweets

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