Interviews aren't only about technical skills.

Here are some questions to help you prepare.

🧵👇

Explain what you have been working on for the past few weeks.

What are the most exciting parts about that work?

What portion of it do you consider boring and why?

[2 / 10]
What specific libraries and frameworks are you familiar with?

What's the minimum set of libraries and frameworks that you'd recommend to any practitioner?

[3 / 10]
What kind of problems have you worked on in the past?

Can you list the specific use cases related to each one of these?

[4 / 10]
What is the most exciting project you have ever worked on?

What was your role and responsibilities on that project?

Why do you think your work on that project was important?

[5 / 10]
What are some of the common issues that you have faced before while working on a project?

How have you approached these problems?

[6 / 10]
How would you organize a team to create and deliver end-to-end applications?

What specific roles would you include in that team?

How would be the interaction among team members?

[7 / 10]
What are some of the specific areas where you'd like to do more research to improve your knowledge and skills?

How do you keep your skills fresh nowadays?

[8 / 10]
When dealing with non-technical stakeholders, what tries your patience?

How do you deal with these situations?

[9 / 10]
If we imagine one year in the future, and we are high-fiving because you succeeded in this position, can you list what went well?

How about if you failed? What went wrong?

[10 / 10]
Hope this helps.

If you want more content on software engineering, machine learning, and adjacent topics, give me a follow. I post threads like this every week.

You can enjoy more of this content here: @svpino.

More from Santiago

10 machine learning YouTube videos.

On libraries, algorithms, and tools.

(If you want to start with machine learning, having a comprehensive set of hands-on tutorials you can always refer to is fundamental.)

🧵👇

1⃣ Notebooks are a fantastic way to code, experiment, and communicate your results.

Take a look at @CoreyMSchafer's fantastic 30-minute tutorial on Jupyter Notebooks.

https://t.co/HqE9yt8TkB


2⃣ The Pandas library is the gold-standard to manipulate structured data.

Check out @joejamesusa's "Pandas Tutorial. Intro to DataFrames."

https://t.co/aOLh0dcGF5


3⃣ Data visualization is key for anyone practicing machine learning.

Check out @blondiebytes's "Learn Matplotlib in 6 minutes" tutorial.

https://t.co/QxjsODI1HB


4⃣ Another trendy data visualization library is Seaborn.

@NewThinkTank put together "Seaborn Tutorial 2020," which I highly recommend.

https://t.co/eAU5NBucbm

More from Life

1/“What would need to be true for you to….X”

Why is this the most powerful question you can ask when attempting to reach an agreement with another human being or organization?

A thread, co-written by @deanmbrody:


2/ First, “X” could be lots of things. Examples: What would need to be true for you to

- “Feel it's in our best interest for me to be CMO"
- “Feel that we’re in a good place as a company”
- “Feel that we’re on the same page”
- “Feel that we both got what we wanted from this deal

3/ Normally, we aren’t that direct. Example from startup/VC land:

Founders leave VC meetings thinking that every VC will invest, but they rarely do.

Worse over, the founders don’t know what they need to do in order to be fundable.

4/ So why should you ask the magic Q?

To get clarity.

You want to know where you stand, and what it takes to get what you want in a way that also gets them what they want.

It also holds them (mentally) accountable once the thing they need becomes true.

5/ Staying in the context of soliciting investors, the question is “what would need to be true for you to want to invest (or partner with us on this journey, etc)?”

Multiple responses to this question are likely to deliver a positive result.

You May Also Like

THREAD: 12 Things Everyone Should Know About IQ

1. IQ is one of the most heritable psychological traits – that is, individual differences in IQ are strongly associated with individual differences in genes (at least in fairly typical modern environments). https://t.co/3XxzW9bxLE


2. The heritability of IQ *increases* from childhood to adulthood. Meanwhile, the effect of the shared environment largely fades away. In other words, when it comes to IQ, nature becomes more important as we get older, nurture less.
https://t.co/UqtS1lpw3n


3. IQ scores have been increasing for the last century or so, a phenomenon known as the Flynn effect. https://t.co/sCZvCst3hw (N ≈ 4 million)

(Note that the Flynn effect shows that IQ isn't 100% genetic; it doesn't show that it's 100% environmental.)


4. IQ predicts many important real world outcomes.

For example, though far from perfect, IQ is the single-best predictor of job performance we have – much better than Emotional Intelligence, the Big Five, Grit, etc. https://t.co/rKUgKDAAVx https://t.co/DWbVI8QSU3


5. Higher IQ is associated with a lower risk of death from most causes, including cardiovascular disease, respiratory disease, most forms of cancer, homicide, suicide, and accident. https://t.co/PJjGNyeQRA (N = 728,160)