the conversations were stupid.
I thought they would be a walk in the park because who wouldn't want to hire me? I am very happy to work with him!
The truth is that you can't support moral discussions – believe me, I've tried and failed miserably.
Many people ignore the ethical discussions and focus only on the technical aspects of their applications.
It's easy to think that because data science and machine learning are technical fields, interviewers and companies only care about your technical skills.
This cannot be further from the truth.
I've seen people get hired just because they were a “cultural fit” and the hiring team really liked the way they did their job.
My friend Mandy Liu he even got a $30k raise and was promoted from senior to lead data scientist before he was hired for his work on behavioral interviewing.
So, in this article, I want to break down specific strategies and frameworks for you to do the same.
Let's get into it!
What are Behavioral Interviews?
Ethical questions are to assess whether your values align with those of the company and whether the company can provide you with the space to thrive and deliver your best work.
This interview is the first step in the entire process and is usually conducted by the hiring manager.
This shows the importance of ethical discussions.
The hiring manager wants to make sure you are someone they can work with and the rest of the team.
It is also a rating interview.
You do badly, and they might demote you from top to mid level.
Do it well, and they may promote you from the top to the top, as happened with Mandy.
Now let's look at the key tips you need to ensure you get hired and increase your chances of promotion.
The Story Vault
The first step is to think about the stories you will use in the interview.
Behavior is about how you work and why you do certain things. This will require you to call upon examples from past experiences to survive.
So, I want you to sit down and review your progress to identify the 2–3 most impactful, longest, and most interesting projects you've completed.
It doesn't always have to be data science or work-based, but it's usually best to keep them relevant to the field, as they translate better to what the interviewee is investigating.
This will be part of your “newsroom” that you will use for every single interview you do.
This doesn't mean you can't answer questions from other projects or work you've done, but these 2–3 will form the backbone of your answers, and you should know them inside out.
This will prevent any awkward moments in the conversation where you have nothing valuable to use; it's always about preparation.
The goal is to select stories with enough depth and technical detail to support multiple questions.
You don't need a separate story for every moral question, 2 is perfectly sufficient (3 is even better), and you will adjust your answer to fit the question.
If possible, have a story that:
- One about success
- One about failure
- One is about teamwork or leadership
This will help you cover a lot of bases.
Cultural Studies and Ethics
It's unbelievable how many candidates don't research the company before interviewing.
I've interviewed over 50 people for data science and machine learning roles, and it's clear that many haven't researched company values or cultural goals.
So, the obvious first step is to find out the company's values.
This is really easy to do; all you need to do is Google:
“[Company] culture and values”
Many companies have an overarching culture/value principle and many sub-principles.
For example, DoorDash's principles and values are:
- We are leaders
- We are makers
- We are students
- We are one team
These are then broken down into their own sub-systems.
To be honest, most companies have the same principles, they're just written a little differently.
For example, here's what DoorDash actually says:
- We are leaders -> You take initiative and ownership over your work
- We are makers -> You take action and don't wait to be told what to do
- We are students -> You are always looking to improve skills and improve your skills
- We are one team -> You work cooperatively with others
This part is very involved, especially if the company has many cultural/value principles.
Go over all the values and list something in the “story vault” that reflects that value or ritual principle.
At a minimum, you should include examples of the culture/goal value; ideally, you should also include sub-goals.
One example per goal is fine, but two is best if you have time and want to over-prepare.
This shouldn't take you more than one hour, and it doesn't have to be written in full word for word; A bad total character score is enough. It's more than you having an idea of what to say.
IR-STAR-L Framework
Companies, interview coaches and websites will all tell you to use the STAR method when answering interview questions, especially behavioral ones.
The problem is that everyone else is doing this, so you don't differentiate yourself at all.
For those of you unfamiliar, the STAR framework is as follows:
- (S) Condition: What was the situation like?
- (T) Work: What should you have done?
- (A) Action: What did you do?
- (R) Result: What happened because of that
There is nothing wrong with STAR.
But it's not right.
STAR does not directly tell the interviewer that you are a cultural fit.
All you're doing is going through previous work experience, which is completely unrelated to the company you're applying to.
If an interviewer at one company asks you:
Describe the most difficult challenge you have overcome at work.
And another interviewer from a different company asked you the same question. Using the STAR method, your answer will be exactly the same.
That's not good.
This is where the IR-STAR-L the frame enters.
This is the framework that I have used in all my behavioral interviews, modifying the standard STAR method into the R-STAR-L framework, which stands for:
- (R) Repeat: Play the question again to make sure you mean it and show you are engaged.
- (S) Condition: What was the situation like?
- (T) Work: What should you have done?
- (A) Action: What did you do?
- (R) Result: What happened because of that
- (L) Link Back: Explain why this outcome and situation is consistent with the culture and values of the company you are interviewing for.
Add two features:
Let's analyze these further.
Repeat
The reason we want to repeat the question back to the questioner is that:
- Show them that we are participating in the interview
- We know the exact question we are answering
- It gives us more time to think about our answer
I know this may sound simple, but I can't tell you how many times a candidate has started to answer a different question than the one asked. It's sloppy and it's a big red flag.
Don't repeat the question word for word, rephrase it slowly. An example is given below.
Connect Back
This is where the magic happens.
After each answer, link back to their values/values.
However, don't make it too obvious; slide this “linking” naturally, or it will look scribbled and desperate.
Most people will be able to read between the lines to understand what you are doing.
The reason we link back is because we want to show them that we really are a “culture fit” for their company.
I mean, who wouldn't want to hire someone who works the way the company wants them to?
By linking/mapping our response to their specific cultural/value criteria, we fix it and make it obsolete.
You tell them exactly what is in it for them when they hire you.
For example
Let me show you an example.
Imagine you're interviewing at DoorDash for a data scientist position, and they ask you the following question:
“Tell me about the time you saw a problem that was not your responsibility.”
This is your chance to show the “Be the owner” principle.
(R) Repeat
“Just to be clear, you're looking for an incident where I took action to solve a problem or improve a process outside the scope of my project or the definition of a 'ticket', right?”
(S) Status
“In my previous role, I was assigned to create a dashboard in Tableau to track delivery success rates for a specific location. While researching the SQL queries powering the dashboard, I noticed a discrepancy of about 4% of orders being flagged as
Failed Deliverybut they didn't have an associated refund or customer support ticket.”
(T) Work
“Technically, my job was to visualize the data as it existed. However, I realized that if 4% of our data was mislabeled, the dashboard would mislead the working team. I felt it was my responsibility to investigate the root cause of this 'ghost' failure rate before finalizing the project.”
(A) Action
“I dug through the raw JSON logs in Snowflake and found an error in the mobile app.
delivery confirmationevent. If the driver has lost the cell signal at the exact moment of descent, the system changes the situation to automaticFailedeven if the customer received the food.
I didn't just report a bug; I wrote a temporary SQL patch to properly classify those specific orders based on GPS coordinates. I then presented my findings to the Engineering lead with a clear explanation of how this was moving our performance metrics.”
(R) The result
“The Engineering Team made adjustments for the next race
Failed Deliverythe rate dropped to its original level of 1.5%, saving the Operations team from having to launch an unnecessary (and expensive) driver retraining program. It also confirmed that our regional performance data is 100% accurate for the first time in two quarters.”
(L) Link Back (“Own” Angle)
“I tend to look at projects through a product lens rather than just a technical one. If the underlying data is wrong, the dashboard is just a distraction for a noisy Ops team. I'd rather take more time to fix a problem I ran into than post something I know isn't 100% reliable, because at the end of the day, I'm responsible and the owner of data-driven decisions.”
Mic drop.
Notice how I slipped in the word “owner” at the end to clearly show them how I meet that cultural value/goal.
Sometimes it will be difficult to do this exactly, but you should aim to have all your answers organized in this way.
If you are serious about getting your dream data/ML job in the next 3–6 months, I recommend you join Code To Careers.
This is my training program where you will work personally with my team and myself to secure your dream offer.
To apply, click the link below, and I can't wait to talk to you!
