Santiago Profile picture
10 Feb, 25 tweets, 4 min read
Interviews aren't broken.

A lot of people complain about them, yet few have any experience hiring.

This is a rant full of my own biases and limited perspective —a break from machine learning threads.

🧵👇
Building a team is incredibly hard.

Building a good team is even more challenging.

Building a good, diverse team is a nightmare.

👇
Imagine you are starting a new company and you need a couple of developers.

▫️ Where do you find them?
▫️ How do you know they are any good?
▫️ How much do you pay them?

How do you make somebody come and work for you, a nobody?

👇
You can't just throw money at them because you barely have any.

Your talent pool is minimal because you don't live in Silicon Valley.

Most importantly, you don't even know how to tell apart a good developer from a bad one.

👇
What do you do?

Recruiters? They may help.

Unless they don't and send you the first warm body they have on speed dial.

This happens —many times.

👇
Many of those "crazy" job postings that you find online come from companies that don't know how to find good developers.

It isn't an evil CEO trying to hire a rocket scientist with 20 years of experience for a junior position.

A lot of the time, they don't know better.

👇
By the way —making a parenthesis here—, most candidates laugh at these posts, assume the worst, and pass.

This is a mistake.

It would take a single conversation to realize that most of these companies have good intentions and just don't know what they are doing.

👇
If you took that call, you could become the person who builds that team from scratch!

Where would you find a better opportunity to build a great career?

Of course, you didn't see this because you were too busy bashing the company on Twitter to get 100 likes on your post. 😐

👇
Getting back to the original rant, 10 years ago, at my company, we hired a few developers based on the following criteria:

▫️ They had a computer
▫️ They were motivated to learn

That's it. Literally.

Developers with zero development knowledge.

👇
Was that a dumb decision? Maybe.

At the time, we were desperate. We tried hard to recruit. But we came back empty-handed every time.

Some of those hires flopped. Some thrived.

👇
We grew. We made money. We started working with big clients.

We went from zero interest to a few dozen applications for every open position.

Both extremes are problematic.

We had to make our interview process harder.

👇
No, we never asked for how many ping-pong balls fit in a bus.

But we started adding some structure to our interview process and started asking tough questions.

Reasonable, but challenging.

👇
When you get 30 applicants for one position that you needed to fill yesterday, you need to find ways to trim the list.

You have to be efficient. You have to be decisive.

You can't afford to give 30 people 90-day contracts to decide who's the best.

👇
Another misconception: "Ask for a Python developer with AWS experience, and that's what you will get."

No, you won't.

A lot of people don't care about the job postings. They blast their resumes to every single place that'll take it.

You are left to deal with that.

👇
I've gotten many applications from grossly unqualified people. Those make the process worse for everyone else, but they are easy to weed out.

Others are equally unqualified but interview really well. Those are a nightmare. (False positives are the worst.)

👇
Let me make a pause for a second:

You spend weeks setting up a fair interview process, hours crafting a good job post, and money to put it out there.

And a lot of people don't even spend a second reading it and send their applications anyway...

👇
...and then, as soon as you ask them a programming question, it turns out that you suck because "it doesn't represent what the real work is"?

Really?

You would think that READING is indeed part of the job, and yet...

👇
I've always worked in small companies.

Can you imagine hiring people when you are Google, Amazon, Microsoft, Netflix, Apple, ...?

How many applicants do they get?

How do you decide who is good and who isn't?

👇
No, when you are a large company, you can't design your interview process based on "what the real job looks like" because you don't know what that will be!

Heck, even for small companies, that's very hard to determine beforehand!

👇
Here is something important, however:

There's a huge problem with diversity in our industry.

Everyone —small, medium, and large companies— must do more to ensure we build more diverse teams.

👇
The solution is not to wait for women and minorities to come and knock on our door.

We must reach out and actively make diversity a priority across our industry!

We must!

👇
If you are actively looking for a job:

Complaining because some companies "ask tough questions" will not get you hired.

It means they have a lot of demand. It's a good thing they are building a good team!

👇
If you aren't into those questions, you can always apply for a smaller company.

Lower compensation, fewer benefits, but hey! An easy interview process!

There are a lot of choices out there.

👇
Don't forget to start reaching out to those "ridiculous" job postings.

99% of people are laughing at them on Twitter. Be the one that gets the job!

👇
If you are building a team, I see you 👀! It's a lot of work.

Keep working at it, and hire more people that don't look like you!

(And throw away every resume from those who clearly didn't read the application.)

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More from @svpino

11 Feb
Today let's talk about why we keep "splitting the data" into different sets.

Besides machine learning people being quirky, what else is going on here?

Grab your coffee ☕️, and let's do it!

🧵👇
Imagine you are teaching a class.

Your students are getting ready for the exam, and you give them 100 answered questions, so they prepare.

You now need to design the exam.

What's the best way to evaluate the students?

(2 / 19)
If you evaluate the students on the same questions you gave them to prepare, you'll reward those who just memorized the questions.

That won't give you a good measure of how much they learned.

😑

(3 / 19)
Read 21 tweets
9 Feb
Seriously though, how the heck can a computer recognize what's in an image?

Grab a coffee ☕️, and let's talk about one of the core ideas that makes this possible.

(I'll try to stay away from the math, I promise.)

👇
If you are a developer, spend a few minutes trying to think about a way to solve this problem:

→ Given an image, you want to build a function that determines whether it shows a person's face.

2/
It gets overwhelming fast, right?

What are you going to do with all of these pixels?

3/
Read 26 tweets
7 Feb
Everything you need to know about the batch size when training a neural network.

(Because it really matters, and understanding it makes a huge difference.)

A thread.
Gradient Descent is an optimization algorithm to train neural networks.

The algorithm computes how much we need to adjust the model to get closer to the results we want on every iteration.

2/
We take samples from the training dataset, run them through the model, and determine how far away our results are from the ones we expect.

We call this "error," and using it, we compute how much we need to update the model weights to improve the results.

3/
Read 19 tweets
5 Feb
The one million dollar question:

"Is it reasonable for someone to dive into machine learning with a shallow knowledge of math?"

▫️ The short answer is "yes."
▫️ The more nuanced answer is "it depends."

Let me try and unpack this question for you.

🧵👇 Image
You can think about machine learning as a spectrum that goes all the way from pure research to engineering.

The more you move towards a research position, the more you can benefit from your math knowledge. If you move in the other direction, you'll get away with less of it.

👇
I have friends that got a Ph.D. and became college professors.

For them, math is an absolute requirement!

Not only are they working on research projects, but they are teaching the next generation of scientists and engineers.

👇
Read 11 tweets
4 Feb
I built a model to predict whether you'll be involved in a crash next time you get in a car.

And it's 99% accurate!

Allow me to show you...👇
Here is the model:

👇
The National Safety Council reports that the odds of being in a car crash in the United States are 1 in 102.

That's a probability of 0.98% of being involved in a crash.

Therefore, my silly model is accurate 99% of the time!

See? I wasn't joking before.

👇
Read 21 tweets
2 Feb
For the past few months, I've been trying to improve the quality of the content I publish.

There are a couple of ways I'm measuring this:

▫️ Efficiency
▫️ Engagement

Efficiency is about how many impressions and followers I get for every tweet I post.

👇
I've gone from posting 3,126 tweets back in August down to 949 tweets last month.

I've cut a lot of the noise!

During the same period, I've doubled my impressions (up to 14.4M last month,) and I'm now converting 5.38 followers for every tweet (up from 2.52.)

👇
The second way I'm watching the quality of the content I'm posting is through the engagement rate.

This has gone down quite a bit since August (almost cut in half!)

As impressions increase, the more pressure I have to put engaging content out there.

👇
Read 7 tweets

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