u/Ok-Airline-8523

Most data science interview processes rely on some form of a take home project. These are often for showcasing your ML and decision-making skills. As someone who has spent a lot of time reviewing these types of projects, the things that have typically led to progressing in the process include the following:

• Business-relevant feature engineering
• Always comparing various algorithms, ranging from less complex yet highly interpretable to very complex and nominally interpretable
• Presentation-ready EDA visuals
• Commentary for all decisions made
• Illustration of letting the data guide at least 1 major decision and at least 2 minor decisions

Bonus points for the age of AI - call out where you use AI. It doesn't make you look lesser than your peers. If anything, it boosts confidence in your resourcefulness with modern tooling.

I started building The Dollar Web a couple of years ago.

Over the past 6 or so months, I've seen numerous other sites pop up advertising similar services. I actually don't mind the competition. I've spent countless hours being thorough with testing, gathering feedback, and focusing on user experience.

I've posted about the site on Reddit and quickly learned that this category has left a bad taste in many people's mouths. Without even visiting the site, I've had several people accuse it of being a thoughtless pile of AI slop. What's worse, I've had some Redditors go to the site and blindly give various tools on the platform a 1-star rating with zero use.

Yes, I use AI for coding.

I also have a LLC, business insurance, auth benefits, a premium model with real paying users, cloud backup for progress tracking, applications that create business, education, event planning, and productivity artifacts, and a growing number of repeat visitors and daily users.

When you see the privacy-first, browser-based tool bundle pitch, is your immediate reaction that this must just be AI slop?

PS - I did not use AI for any of this post. If it's slop then it's human slop.

I've been using Supabase pretty heavily for the past couple of years, but realize I haven't tested out other alternatives.

For anyone who has used other options (e.g., Firebase), what are the pros and cons of Supabase relative to the alternative? Would you recommend that I explore a different solution for a new website I'm developing?

I absolutely love hearing different takes on this topic. From SQL to modeling, some people are convicted that AI can't replace what they do while others believe it's just a matter of time.

I personally think that the safest bets are in the soft skills family: stakeholder collaboration, presentation & communication, and business judgement.

What do you think?

A lot of the work that determines whether a model will be successful happens before training ever begins.

Refining the dataset so it resembles what the model will actually see in production can sometimes be more an art than a science.

This can entail removing predictors that won't realistically be available at prediction time, cleaning data, and applying any transformations that need to be consistent between training and inference. Since many ML models are applied to complicated real-world environments, it can really take domain expertise to get this right.

The list where this can go south is long, but here's a few:

• Overfitting to how the training data was collected
• Unintentional tautological bias
• Training-serving skew
• Data leakage
• Temporal leakage

What else?

1. Understand how predictions will be used before building anything
2. Learn SQL well
3. Adopt a solution engineering mindset
4. Feature quality > algorithm tuning
5. Obsess over business value
6. "I don't know but I'll find out" is often the best answer as long as you follow through
7. Don't ignore classic statistics as an option to solve a problem
8. How you show up is as important as what you accomplish
9. Translate model performance into business performance every time you discuss a model
10. If your EDA forces you to go back to feature engineering then it's working
11. Generalization is the point of a model, so be methodical in how you split your data for training, testing, and validation (yes, all three)
12. Actively worry about overfitting
13. Data is beautiful, so label your axes, use proper number formatting, and please do not ever show a graphic that has column names in it with underscores (e.g., GENDER_MALE)

What else?

Shortly after getting my masters in applied math, I completed a 6 month machine learning bootcamp. I found that everything I learned during this program was just an extension of what I learned in my MS program, especially the statistics courses.

This was over a decade ago, and I've been practicing data science ever since. While I eventually went into management and even got an MBA, I never stopped contributing hands-on. I never wanted to lose my skills. This meant coding in SQL, Python, and R, and specifically solving complex data problems using a variety of advanced techniques including but not limited to (un)supervised learning.

That's what data science is to me. It's pairing your technical savviness with business acumen to solve real problems in a measurable way.

I've met "data scientists" who just build dashboards, only write SQL, or live in Excel. What does being a data scientist mean to you in 2026?

For "static" websites with light-weight backends, I found the stack of Netlify + Supabase to be super intuitive and easy to create a website (especially using AI).

What's a good stack to use if you want a heavier weight backend that handles real-time social interaction, reviews, and file uploads? Can you share high-level steps that you would take if you were to start building a website in 2026 that had those requirements?

Appreciate the insight!

There's no debate at this point that AI makes you more productive (if you disagree then you're welcome to comment why). What I am more willing to debate is, does the productivity gain from using AI buy you more leisure time in your day, or less?

To me, AI accomplishes 2 high-level things, regardless of what field you are in:

1. Greater efficiency in existing work
2. Entirely new capabilities

If all it did was 1) above then I would have SO much more time in my day for personal things. The reality of 2) is that it actually creates more work for me in my day-to-day. So, ironically, using AI leads to being more busy, not less.

What are others experiencing?

I built my first deep learning model in 2017 using TensorFlow 1.0. Anyone who practiced during that time knows how much of a pain it was.

Before AI came around, I wrote so much code. Cross-validation, tuning grids, and limitless feature engineering pipelines. Helper functions for days. It really was a blessing to have to go through all of that because I now have a significantly deeper appreciation for what AI can and should do.

I lead a data science org, and despite what you might be hearing about the field, I'm still hiring. The roles aren't going away, but what I screen for has completely changed:

1. Problem formulation. The most expensive failures I've seen weren't bad models - they were great models answering the wrong question. AI doesn't save you from that. It just lets you build the wrong thing faster and with more confidence.
2. Knowing when the model is lying to you. AI will happily hand you a pipeline with subtle leakage or a validation split that flatters you, and it all runs without errors. The skill isn't writing the pipeline anymore. It's smelling that a 0.96 AUC is too good to be true and knowing the five most likely reasons why. That instinct only comes from having been burned.
3. Owning the decision, not the notebook. Those who get promoted can sit in a room where a leader is about to make a bad call, show what the data actually supports, and change the outcome. That was true in 2019 too.

For those earlier in your careers, this is better news than the doom posts suggest. The moat used to be years of grinding through boilerplate. Now it's judgment, and you can start building judgment on day one.

Curious what others who are hiring are seeing.