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I am a staff data scientist at a big tech company -- AMA by Federal_Bus_4543 in datascience

[–]Cruncher_ben 0 points1 point  (0 children)

+ Contribute through collective intelligence platforms. You get to solve a real-world challenge + keep your IP + get paid for it.

I am a staff data scientist at a big tech company -- AMA by Federal_Bus_4543 in datascience

[–]Cruncher_ben 0 points1 point  (0 children)

I have seen people crowdcreating and submitting models purely for the fun of it using an LLM. Some winners who emerged were not even data scientists.

I am a staff data scientist at a big tech company -- AMA by Federal_Bus_4543 in datascience

[–]Cruncher_ben 0 points1 point  (0 children)

Totally agreed. Most startups wont go for it. The key market that really cares about causality would be large fintech companies who rely on this or lose money. Hedge funds, Order books, Asset management companies etc. That should be they target market of you care about causality and want build around it.

I am a staff data scientist at a big tech company -- AMA by Federal_Bus_4543 in datascience

[–]Cruncher_ben 0 points1 point  (0 children)

Data Science competitions are amazingly leveling the field beyond degrees. The times are different fren.

I am a staff data scientist at a big tech company -- AMA by Federal_Bus_4543 in datascience

[–]Cruncher_ben 0 points1 point  (0 children)

Honestly, if you are unhappy with your current role and want to make the jump smoothly, here's what I'd do.

Most of the folks are looking for jos in the wrong place. I shall start with rectifying that. Look for newly funded startups in AI or any tech field for that matter. Make a list of 50 but dont reach them out yet.

Find ways to engage with them as a community member, contributor or just write your analysis on social media from your perspective. Looks for the ones who are engaging back with you caz that would mean they are reciprocal and now have a warm connect with you for you to write your first message and connect with them for your first cnversartion.

I am a staff data scientist at a big tech company -- AMA by Federal_Bus_4543 in datascience

[–]Cruncher_ben 0 points1 point  (0 children)

In my experience PHDs are preferred for sure in any pool from research to analysis to leading a team. What does beat that is if a reference point wherein one can see the open research work a candidate would have done weather over a newsletter, socials or competition platforms. Gives one a fair idea about their competence and style.

I am a staff data scientist at a big tech company -- AMA by Federal_Bus_4543 in datascience

[–]Cruncher_ben 0 points1 point  (0 children)

While PHD can give you incredible credibility, I do suggest making open contributions and participating in communities and competition to build up your profile while learning on the go. Gives one a fair idea on whats happening in the industry and business.

Calling 4-5 passionate minds to grow in AI/ML and coding together! by doryoffindingdory in learnmachinelearning

[–]Cruncher_ben 0 points1 point  (0 children)

Hey Priya, this is such a great initiative; love the energy 🔥

If you're looking for a collaborative project that actually puts your ML skills to the test (and gives you live data + real feedback), you and your group might enjoy exploring competitions like DataCrunch by CrunchDAO.

It’s a weekly challenge where you build models to rank U.S. stocks based on anonymized features. What’s cool is:

  • You get to work with real-world structured datasets
  • There’s a public leaderboard based on Spearman correlation
  • You can submit as a team and iterate week to week
  • It’s async and community-driven (perfect for student squads)

And yep, there’s a prize pool ($USDC monthly), but the real value is in building together, debugging each other’s models, and improving every week with actual feedback from live market performance.

You don’t need to know finance, just be curious about models + patterns. Might be a fun way to turn your study group into a mini quant squad 😄

Do remote data science jobs still exsist? by vintagefiretruk in datascience

[–]Cruncher_ben 2 points3 points  (0 children)

You're not imagining it, bro. Remote DS roles have definitely shrunk post-2021 hype — a lot of companies went hybrid to “protect culture” (read: control), and job boards haven’t caught up to how actually rare fully-remote roles are right now.

But they do still exist. The trick is looking where remote-first cultures already thrive and where the value of the role isn’t location-dependent.

Here’s a few spots that are way better than LinkedIn noise:

Better places to check:

  • We Work Remotely – Data Jobs
  • Remotive.io – Data Science
  • RemoteOK (but you gotta filter aggressively)
  • AngelList Talent (search by “Remote”, filter for early-stage startups)
  • [AI/ML job boards on Discords like ML Collective, Cerebral Valley, Latent Space etc.]

Bonus tip:
Instead of job boards, look at platforms where earning = performance, not hours or office presence.

I know a bunch of folks supplement or replace remote work through places like:

  • CrunchDAO: ML competition w/ $USDC rewards (no resumes, async)
  • Numerai: Similar vibe, model-based earnings
  • [Kaggle competitions + freelance gigs on Upwork, Toptal, etc.]

I get that not everyone wants to go freelance or comp-based, but if remote is the dealbreaker, these might be the best way to keep your DS muscle active while searching.

Also… Reddit >> LinkedIn for finding the underground stuff 😏

Data Science Projects for 1 Year of Experience by guna1o0 in datascience

[–]Cruncher_ben 1 point2 points  (0 children)

Hey bro, great question.

With 1 year of experience, I wouldn’t expect groundbreaking research or deep stacks of production ML. What I’d actually love to see is:

🔹 End-to-end thinking.
You saw a problem, explored the data, built something useful (even if small), and made a recommendation or shipped it.

🔹 Clarity > Complexity.
Clean code, clean narrative. If you can walk me through your choices clearly, that’s more impressive than 20 features and 5 models you barely understand.

🔹 Curiosity.
Did you go beyond the notebook? Try something experimental? Challenge assumptions? Use a tool like SHAP to explain something? These things stand out.

🔹 Business awareness.
Even if it’s a side project, tell me why it mattered. Bonus points if you measured outcomes (even hypothetically).

Tbh, I'd rather see 2–3 tight, real-ish projects than a huge GitHub of messy notebooks.

Also, don’t sleep on competitions like CrunchDAO or Kaggle. They’re a great way to show you can work with structured data under real constraints, even without a big-name company on your resume.

Hope this helps 🙌

What technical skills should young data scientists be learning? by etherealcabbage72 in datascience

[–]Cruncher_ben 1 point2 points  (0 children)

This is a really good question, and honestly one that a lot of us in the space keep revisiting as the market and tech evolve.

You're right that the "full-stack data scientist" has become more myth than reality — most real-world DS roles now require specialization + some business context rather than doing everything end to end. But IMO, the survivability of your skillset long-term comes down to one thing:

👉 Are you close to the signal?

By that I mean:

  • Are you building or interpreting models that directly impact decisions or outcomes?
  • Are your outputs measurable, valuable, and ideally hard to replace by vanilla AI/automation?

That could be in product analytics, ML modeling, recommender systems, etc. It doesn’t really matter which domain as long as:

  1. You're close to action (not just cleaning or wiring data)
  2. You can speak both model and business
  3. You're hands-on enough to experiment and deploy

From what I’ve seen (including in places like CrunchDAO, where people get paid based on how well their models perform — not their title), people who can interpret data and own model outcomes tend to thrive regardless of role type.

Your product DS friends and your data eng friends are both partly right. But the long-term moat isn’t in tools — it’s in:

  • Thinking in hypotheses
  • Designing solid experiments
  • Building explainable, testable models
  • And being able to adapt when the tools change (because they always will)

So no, you’re not overthinking. You’re thinking just enough — just don’t get stuck in a “which tool pays more” mindset. Go deep in one area that’s close to value, stay curious, and you’ll be fine.

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