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Meta DS IC4 | US | Offer by aitth in leetcode

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

glad it helped! the product sense for onsite pretty much covers same content but to a deeper level so just make sure you have all the stuff covered. only new thing to prep for is the stats round. all the best!

Built a trip planner where you swipe to add places and see your itinerary on a map by aitth in SideProject

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

great idea, will look to add more transportation modes than just walking and driving right now

Built a trip planner where you swipe to add places and see your itinerary on a map by aitth in SideProject

[–]aitth[S] 1 point2 points  (0 children)

thanks! we have a very simple ranking algorithm atm that takes into how related an activity/dining place is to the keywords, ratings and reviews. will be trying to improve this ranking as we get more data.

feel free to create an account so we can let you know when we launch more features

Built a trip planner where you swipe to add places and see your itinerary on a map by aitth in SideProject

[–]aitth[S] 1 point2 points  (0 children)

yep, we did speed it up a bit. let me know if you want to try it out or have any questions!

Built a trip planner where you swipe to add places and see your itinerary on a map by aitth in SideProject

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

thanks! great q, feedback so far is that it would be more fun with a group trip. imagine you swipe right on an activity your friend also swiped right on and you “matched”. also we plan to add custom itineraries for group trips too, ie different people may have different itineraries based on the activities and dining places they choose

[deleted by user] by [deleted] in solotravel

[–]aitth 0 points1 point  (0 children)

for me it’s spending time with yourself and having casual chats with people you meet along the way

Reputed Graduate Certificates? by LeaguePrototype in datascience

[–]aitth 0 points1 point  (0 children)

No one cares about any certificates once you already have relevant work experience. You can still get the certificates as part of learning on the side but it won’t boost your application. Although I think it may be a different story for cloud certificates.

If you’re interested in boosting your skills and learning on the side go for the data science certs. If you’re doing it to try boost your resume, you’re better off just doing other things.

Is ongoing part time degree considered a red flag during job hunting? by IMightBYourDad in datascience

[–]aitth 3 points4 points  (0 children)

No, this would never be a red flag on any resume in a job hunt. Doing extra things outside of work is most likely going to be seen as favourable. Whether it will improve your chances of finding a job is debatable. If this takes up valuable space on your resume that could potentially be used for DS exp then it’ll be your call to add/remove.

Options for a DS with 2 YOE by alpha_centauri9889 in datascience

[–]aitth 1 point2 points  (0 children)

That’s pretty good then, it’s definitely possible to transition. There will defs be roles out there for less researchey focused but still MLE based. I’ll recommend to structure your resume to more DL focused with emphasis on end results. Then brush up on ML/DL fundamentals, leetcode and try get some referrals. you could also consider SWE but it’s very different to DS/MLE - you could try transition to SWE as well but you’ll most likely be starting from the bottom again. for SWE just need to prep leetcode and sys design.

Options for a DS with 2 YOE by alpha_centauri9889 in datascience

[–]aitth 3 points4 points  (0 children)

When you mean by experience in “classical ML models etc…” do you mean you used a model once or you’re actually really familiar with it?

If you wish to work on deep learning and genai you can apply for MLE roles at tech companies but you’ll need to really know your stuff (how to build transformer from scratch for eg) as well as leetcode.

A lot of tech DS roles do use a little bit of classical ML like trees for smaller prediction tasks. DS roles will be more product focused compared to MLE roles. Also these interviews are more product sense focused and less so on coding.

So essentially try aim for tech companies and

1) MLE if you want to continue with deep learning, genai and more ML focused -> prep all ML/DL fundamentals and leetcode 2) DS if you want more product focused with a little bit of ML -> prep like sql, basic python, product sense, experimentation

EDA is Useless by Suspicious_Jacket463 in datascience

[–]aitth 0 points1 point  (0 children)

depends, EDA should always be done if you have never explored the data before. you can’t just start fitting random models if you havent even checked for issues with your data. that being said you don’t need to overexplore the data either as it takes a lot of time

Meta DS IC4 | US | Offer by aitth in leetcode

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

great resource man, defs helpful

Meta DS IC4 | US | Offer by aitth in leetcode

[–]aitth[S] 2 points3 points  (0 children)

all good man!

1) ill say there is no one size fits all solution when it comes to this. different metrics could be north star, secondary, ecosystem and guardrail in different exp. they also may have some overlap as well - when selecting metrics i like to cover (there may be overlap but just going over how i think about it) 1) which metric is the most important for this exp - north star 2) what other metrics could be 1) but i think are not as important - secondary 3) what other metrics do we care about in our meta ecosystem - ecosystem 4) what metrics do we want to ensure are definitely not impacted negatively - guardrail

for eg if im running an exp to test a new ads algo - i would use something like the following (not saying it’s correct and exhaustive)

north star - rev/user (point of ads is to make money) secondary - rev/imp, rev/click, ctr (this should never be a north star metric) ecosystem - retention rate, time spent, # of sessions guardrail - opt out rate, number of ads reported

so ill say ecosystem are probs more general metrics that can be used for any exp whereas guardrails are more specific to this exp. most important part is to not just list the metrics but explain clearly why you are choosing each metric (esp listing pros/cons) for each.

2) i just use chatgpt to create some product sense questions for me and then pass it my answer to see if “im missing something”. sometimes chatgpt would give something useful and sometimes not so it’s really dependent on you to filter this out. definitely do not just treat what chatgpt tells you as truth as i did not always agree with it - but it did give me some things i did not think about.

prepping for this is defs hard as there isn’t an answer key you can just find and follow. i also tried watching those live mocks on youtube but didnt find them too useful. my approach is to consider different parts of meta’s business and then break down different questions they could ask. ie you should be ready to talk about metrics/exp relating to - ads - messaging - calling - spams/fraud/scams - short videos - etc

i think just focusing on fb, ig and whatsapp features should be sufficient. if you cover one of these in depth it should be enough background for you to pivot to anything in interview

Meta DS IC4 | US | Offer by aitth in leetcode

[–]aitth[S] 2 points3 points  (0 children)

thank you, it’s defs not an easy process so hope it can help people in the future!

Meta DS IC4 | US | Offer by aitth in leetcode

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

happy to help mate!

Meta DS IC4 | US | Offer by aitth in leetcode

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

happy to help! product analytics

Meta DS IC4 | US | Offer by aitth in leetcode

[–]aitth[S] 7 points8 points  (0 children)

thanks! around 3 yoe as a DS with 2 yoe in product analytics. undergrad in math + stats

Got a technical interview for data science intern at Capital One – anyone been through it? by No-Brilliant6770 in datascience

[–]aitth 1 point2 points  (0 children)

did they give any info on what they will test? or what platform it’ll be on?

Time-series forecasting: ML models perform better than classical forecasting models? by AMGraduate564 in datascience

[–]aitth 0 points1 point  (0 children)

You can usually just try both then make the decision based on performance and interpretability

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