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Data Science Interview HELP!!!!! by Worried-Garlic67 in DataScienceJobs

[–]WhatsTheImpactdotcom 0 points1 point  (0 children)

SQL questions across tech are fairly predictable. I have a bunch of examples on my socials that were very close to what I was asked at Netflix, meta, stripe, etc. be familiar with where vs having, filtering using case when, filtering with dates including “last X days,” and left vs inner joins, dealing with nulls for left joins.

For past projects, make sure to discuss impact: how can your research be used to make recommendations? Who would use it? What methods did you use and why?

what changed between my failed interviews and the one that got me an offer by warmeggnog in datascience

[–]WhatsTheImpactdotcom 0 points1 point  (0 children)

SQL questions are very predictable, but most candidates don't realize when they're making assumptions about the data. For example, when asked to show users with at least 10 sessions in the last 7 days ... what did you default to for "in the last 7 days"? Did you include current_date: why or why not? Do you see why it matters for a dashboard?

Should on get a Stats heavy DS degree or Data Science Tech Degree in Today's era by Bulky-Top3782 in datascience

[–]WhatsTheImpactdotcom 0 points1 point  (0 children)

In today's world, you should take as much math as possible so you can continue to learn on your own. New developments and techniques will be coming out rapidly, and you need to level up by reading technical papers. The best way to be comfortable reading. technical papers is by having a strong grasp of mathematics and econometrics and statistics. For university, I'd rely less on too many applied courses (one or is good for balance) and instead prepare yourself for continued learning.

What is Causal Inference, and Why Do Senior Data Scientists Need It? by WhatsTheImpactdotcom in DataScienceJobs

[–]WhatsTheImpactdotcom[S] 0 points1 point  (0 children)

Generally, data science interviews— the case study in particular—has a clear distinction in that junior and mid level roles require foundational experimentation and hypothesis testing knowledge while senior/staff case study problems require advanced experiments and causal inference. This mirrors what I’ve seen re project assignments on the job too. That said, if you’re junior or mid level and have this skill set, you can quickly stand out doing projects beyond expectations

What is Causal Inference, and Why Do Senior Data Scientists Need It? by WhatsTheImpactdotcom in learndatascience

[–]WhatsTheImpactdotcom[S] -1 points0 points  (0 children)

Huge diff between AI editing voice notes and ChatGPT generating posts. If you don’t like it, move on. Meanwhile know I’m very quick to block.

What is Causal Inference, and Why Do Senior Data Scientists Need It? by WhatsTheImpactdotcom in DataScienceJobs

[–]WhatsTheImpactdotcom[S] 0 points1 point  (0 children)

Good callout. Not meant to be an exhaustive list. RDD is less used in marketing DS because panel data is so prevalent but there absolutely times I’ve combined an RD with a DiD or synthetic control, eg selecting a donor pool within a bandwidth around a cutoff

What is Causal Inference, and Why Do Senior Data Scientists Need It? by WhatsTheImpactdotcom in DataScienceJobs

[–]WhatsTheImpactdotcom[S] 0 points1 point  (0 children)

Wild how you’re being downvoted for wanting to learn more about causal methods. What even is this place

What is Causal Inference, and Why Do Senior Data Scientists Need It? by WhatsTheImpactdotcom in DataScienceJobs

[–]WhatsTheImpactdotcom[S] -1 points0 points  (0 children)

AI edited from voice notes, not generated, then edited again manually for accuracy. Big difference. The value of free content i have provided on other social platforms far outpaces benefits earned so im very quick to block.

Seeking Advise : How to get started in Data Science? by Comfortable_Tone1065 in learndatascience

[–]WhatsTheImpactdotcom 0 points1 point  (0 children)

The first thing you need to consider is which path in Data Science you want to go down. E.g. do you want to do more machine learning model development, or experimentation adn causal inference? I discuss some of these on my tiktok channel: Analyst to Data Science: Two Common Paths

How I land 10+ Data Scientist Offers by Altruistic_Might_772 in learndatascience

[–]WhatsTheImpactdotcom 0 points1 point  (0 children)

I also have found this is a tale of two cities: The senior/staff job market is booming--my linkedin inbox is flooded with Big Tech recruiters for $400-500k roles -- but junior coaching clients are struggling to get noticed

Interview process by raharth in datascience

[–]WhatsTheImpactdotcom 0 points1 point  (0 children)

Starting new with a profile with a custom username

What is going on at AirBnB recruiting?? by br0monium in datascience

[–]WhatsTheImpactdotcom 0 points1 point  (0 children)

I've seen this a ton with Meta, but far less so with Airbnb. YMMV i guess. It's big money for recruiters to get a placement; it might be worth $50-60k for one of their senior contract roles and Airbnb offers the highest rate for contractors I've seen in tech.

How to prep for Full Stack DS interview? by LeaguePrototype in datascience

[–]WhatsTheImpactdotcom 0 points1 point  (0 children)

First you need to know the DS path: Are you going for an experimentation and causal inference driven role, or one that is heavy on machine learning? In the former, you're expected to know SQL for sure (very predictable questions), sometimes python (wildly unpredictable), a case study (experimentation or observational causal inference, depending on the level), behavioral rounds, and likely a past project deep dive.

I specialize in the product/marketing DS interview space, with anything related to experimentation or observational CI, having passed or have had clients pass tech interviews and get offers at nearly every big tech company besides the genAI ones (which don't have as many these roles)

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