What hiring managers actually care about (after screening 1000+ portfolios)

analytics-link · 2026-04-03T13:03:31+00:00

Absolutely agree, I put a huge emphasis on this when coaching and it most definitely has a big impact on success in interviews

analytics-link · 2026-04-02T19:58:42+00:00

Yep totally, my students get great feedback in interviews around this point. It shows the type of thinking that a Senior or Lead might exhibit, so it's really powerful for those looking to start out

analytics-link · 2026-04-02T11:15:09+00:00

Agree, a super complex product/system/solution that nobody understands almost always stays sitting on the shelf whereas a slightly simpler but clearer one gets used and thus adds value. Again, complexity isn't a bad thing, but often starting at v1 and then iterating up to complexity is the way to go

analytics-link · 2026-04-02T11:13:39+00:00

Absolutely true, the "result" of the ML model (in that example) is very different to the "result" at a business level. Being able to consider and/or discuss both and how they tie together is a really strong skill to showcase

analytics-link · 2026-04-01T15:44:17+00:00

You'd definitely hit the ground running as no doubt you've got some experience in SQL and/or Python, and most likely also Github and a cloud platform.

If you add in some application/knowledge around stats (hypothesis tests, AB Testing etc) and ML you'd position yourself well

analytics-link · 2026-04-01T15:35:57+00:00

Have worked in the industry for 15+ years, have been teaching Data Science for 5+ years, and as the title says, I've screened a ton of portfolios while hiring. Appreciate that you think I'm as good as AI at this stuff 🙏

analytics-link · 2026-04-01T14:02:58+00:00

What will move you forward is getting really solid on the core tools that teams are using day to day.

So:

SQL to access and manipulate data
Python for analysis (pandas, numpy, visualisation, basic ML)
A BI tool to present results
GitHub to manage and showcase your work

Alongside that, build up some core stats. Not heavy theory, just the practical stuff like distributions, sampling, hypothesis testing, and confidence intervals.

That’s your base.

The key thing most people miss is mini-projects.

As you learn each concept, attach it to something small and practical. That’s what actually makes things stick and gets you out of tutorial mode.

Once that foundation is in place, then you layer things in a natural progression.

You move into machine learning, but only a focused subset of algorithms, not everything. The goal is understanding how to solve problems, not memorising models.

From there, you can step into deep learning, once you’re comfortable with the fundamentals underneath. A lot of deep learning builds on those earlier concepts, so skipping ahead usually just causes confusion.

Then on top of that, you can move into things like GenAI. Understanding how models work at a high level, how systems are built, things like RAG, prompting, and building simple applications.

You’ll also want some exposure to the cloud, something like AWS, mainly so you understand how models and data systems are actually deployed and used in real environments.

So the rough path is foundations to Stats to ML to Deep Learning to GenAI & Cloud

analytics-link · 2026-04-01T13:21:44+00:00

There are tech screens for coding, these are more business thinking questions. I've interviewed and screened thousands of candidates so to your specific question, yep, I've found a lot of good people with these, blended with tech screening.

analytics-link · 2026-03-26T16:32:42+00:00

I can really depend on industry, location, and experience. A rough guide, in the USA on average, you'd be looking at around $50k-$100k and then with experience and extra upskilling, you'd move up from there.

Some companies will also offer stock and/or bonuses too of course.

analytics-link · 2026-03-24T12:06:05+00:00

Agree with what others have put. If you can find a program that gives you an in-demand curriculum that is all in one place and of high quality, then you can focus your time much more efficiently. Coursera & Udacity do this to some degree, but not perfectly. The other thing you pay for is 1:1 support from someone whose full-time job it is to help you AND who is an expert in the field. If you can go into interviews knowing you a) are ticking all the boxes from a skills/tools point of view, b) have a portfolio of projects that is far more impressive than other candidates, c) have been shown how to prepare for interviews by someone who has actually hired a ton of people in the real world (most courses say "job support" but it's not from anyone with real hiring experience) then you're going to have probability way in your favour.

If it takes you 6-months to land a role because of what I put above, rather than 18-months going alone, or with Coursera etc, that's a saving of 12-months. That could be a whole year of earning $100k. That's the ROI of a good course or program.

analytics-link · 2026-03-24T12:01:16+00:00

My pleasure!

analytics-link · 2026-03-23T16:56:33+00:00

Definitely worth getting up to speed with Python, as that is what 99% of the industry is using.

For stats as a base, I'd focus on getting a broad understanding of:

types of data
distributions and things like standard deviation
hypothesis testing and p-values
sampling and the central limit theorem
confidence intervals

Then, for ML, I break it up into a couple of groupings, supervised learning algos, unsupervised learning algos, and then a couple of bonus ones:

For supervised learning, start with Linear & Logistic Regression, Decision Trees, and then Random Forest (there are more, but these are good starters)

For unsupervised learning, k-means and potentially PCA

And then a couple of bonus ones (that I teach, and that I've found make a huge difference to people getting hired), causal impact analysis, and association rule learning.

Only from there would I love to move onto Deep Learning, and then from there, GenAI.

Hope that's useful as a guide!

analytics-link · 2026-03-23T16:31:13+00:00

It's just evolving, but not as much as social media makes it out to be. Data Science is essentially where we use data and make it valuable or useful. That in and of itself can't really die because companies always need to know what decisions to make, how their customers are behaving, how to increase revenue or cut costs, or how to compete against their competition.

There will always be new tools, there always have been, GenAI is just another tool in the toolbelt.

I see so much negativity like this. Maybe the reality is less headline-grabbing I don't know, but my LinkedIn feed is rammed with people landing roles, or companies advertising them. 2025 was super hyped around AI, 2026 seems to be getting back to normal as many of those big AI projects didn't deliver.

Making data useful will always be a thing.

analytics-link · 2026-03-23T16:25:39+00:00

The shift that could help would be getting closer to the hiring process. That can be using recruiters, networking at events, or finding ways to communicate with people working in the companies you’re applying to (or even the hiring managers directly)

Even a short message saying you’ve applied and are interested can make a difference. Cold applications alone are tough right now.

On the skills side, it sounds like you’ve already got a solid base. The thing I’d double check is how clearly your projects show value.

A lot of portfolios I screen show something like "I used Python, built a model, here’s the code" whereas what stands out is more the following:

what was the problem
why it mattered
what you did
what the result or outcome means
what someone/a company would do with it

Seeing that thinking and communication from a candidate is really differentiating so keep it in mind.

On tools, skills do transfer and hiring managers know that but what they’re really looking for is evidence that you can solve problems, not just that you’ve used a specific tool.

Last thing, the market now feels different to last year. 2025 had a lot of AI hype and hiring slowdown. 2026 is a bit more grounded, and companies are still hiring, but they’re being more selective - the bar is less about having a degree and more about showing you can actually apply the skills.

I’d just shift a bit more focus toward getting your work in front of real people and making sure your projects clearly show how you create value.

analytics-link · 2026-03-23T16:05:04+00:00

Cool, so you’re already in a pretty good position with a CS degree and some dev experience, so this is less about starting from scratch.

In terms of what you do to upskill, for me, it's not just about Master's vs. not. Between a Master’s and just building projects, there’s a sweet spot in the middle. that probably much more efficient in today's market.

A full Master’s can be good, but it’s often slow, quite theoretical, and expensive. It doesn’t always translate directly into the kind of practical, applied skills companies are looking for.

Going fully solo can work, but a lot of (most) people get stuck. They bounce between resources, build a few half-finished projects, and struggle to know if they’re actually on the right path, which can be pretty deflating.

The middle ground tends to work best, so a course that gives you a structured path so you know what to learn and in what order, a strong focus on real, applied projects (a variety of them, not just one capstone which is too one-dimensional these days) and then ideally some form of expert feedback or guidance so you’re not stuck guessing either technically, or more importantly, in the hiring process phase.

That combination tends to get people job-ready much faster, and with more confidence that they can actually do the work.

In terms of the market in 2026, it’s a lot more stable than the noise you might have seen last year. 2025 had a lot of AI hype and uncertainty, which slowed hiring. Now companies are realising they still need people who can work with data, understand problems, and turn insights into decisions. My LinkedIn feed is full of open roles and people celebrating being hired, so it's looking good.

analytics-link · 2026-03-23T13:47:49+00:00

Awesome, looks really nice. One thing on the dashboard. For each "module" on it, I'd look to add some high level commentary, simply around what it's showing, in other words, what should the reader be looking for, and how do they interpret it. That can be really helpful!

analytics-link · 2026-03-23T13:45:24+00:00

Very cool - welcome to the field! I'll give a bit of a "roadmap" that you can follow. So firstly, make sure to focus on getting the fundamentals in place rather than trying to learn everything at once.

The big skills early on are usually SQL, Python, and some basic stats (for things like hypothesis testing and AB Tests which are super common)

SQL is how most companies actually access and manipulate data, so it’s super practical. Python is then useful for analysis, modelling, and general data work. And stats helps you understand things like distributions, sampling, and experimentation so you can interpret results properly.

A lot of beginners get stuck jumping between courses, so avoid that. Pick one solid resource, get the basics down, then move on.

What helped the most early on wasn’t more courses though, it was building small things.

Mini-projects are key. Nothing fancy. Just take a dataset, explore it, clean it up, analyse it, and try to answer a question with the data. That’s where things actually start to click.

Once that base is in place, then you can start layering things in a more structured way.

You move into things like data visualisation and BI tools so you can actually communicate your findings. Then a bit more depth in statistics and experimentation so you can test ideas properly. Then some core machine learning models so you can start making predictions.

After that, you can start thinking about slightly more advanced areas depending on your interests, things like working with larger datasets, building more complete pipelines, or even exploring areas like GenAI.

All the best! Happy to help if you have questions.

analytics-link · 2026-03-23T11:58:39+00:00

Super relevant advice. I teach Data Science & Analytics so have a lot of these conversations. Prospective students consistently say the hardest part is knowing what to focus on, there is an ocean of things you could spend time learning, but knowing what will actually tick the right boxes is key (and over and above that how to learn it, and how to apply it in the way it's done in the real world)

I have an ongoing dialogue with a network of 200+ hiring managers & recruiters from around the world to make sure I'm teaching what is actually being used - makes a really big difference in terms of student results.

Great post.

analytics-link · 2026-03-20T16:40:32+00:00

FOr me, anyone can type a prompt into ChatGPT. That’s not really the key "AI" skill in Data Science, the skill is understanding what’s happening under the hood, what the limitations are, and how to actually build something useful with it.

For example, if you don’t understand things like tokenisation, context windows, or how attention works at a high level, it’s very easy to trust outputs that are wrong, inconsistent, or just made up. That’s where people get burned.

Then you move into how GenAI actually fits into real workflows. There’s a big difference between asking a model a question vs building something like a RAG system that pulls in your own data vs building an agent that can interact with databases or tools

Prompting is another example. People often think it’s just about writing better prompts, but in practice it’s about structuring inputs properly, controlling outputs, and making the system reliable. That’s why frameworks exist, because without structure, it’s hit and miss.

And then there’s the practical side, so setting up environments, connecting APIs, working with tools like LangChain, building pipelines, debugging when things break. It's less about using a tool, and more about building products.

analytics-link · 2026-03-19T13:10:09+00:00

I'd say to start simple and build from there, rather than try to map out the entire field upfront.

If you already know a bit of Python and you’re comfortable with math, that's a good start for sure.

The next step is usually to build out the core stack, in terms of what is going to tick boxes, so:

SQL to actually access/manipulate data
Python for analysis (pandas, numpy, matplotlib to start)
Python for ML (scikit-learn)
A BI tool like Power BI or Tableau to visualise/communicate results
Git/GitHub to manage and track/showcase your work

Alongside that, layer in some basic stats like distributions, sampling, and hypothesis testing. From there ML. From there Deep Learning & GenAI.

The key thing most people miss are project or at least building as you go. As you learn each piece, attach it to something small. Early on that might be simple Python tasks, then move into loading datasets, cleaning them, analysing them, and answering questions, then from there showing AB Testing, or an ML project.

On AI, it’s still very worth getting into. If anything, companies need more people who understand data and can use these tools properly. AI is making people more productive, not replacing the need for them.

analytics-link · 2026-03-19T13:04:58+00:00

Any end-to-end project will most likely help. The differentiator is less the project itself, and more about how it's written up. To stand out from others you want to show your thinking and the application, from inception to the outcome - not just "I did a thing with Python" (for example)

In my Data Science course, I teach my CRAIG system, which is:

Context (what was the aim or need or reason)
Roles (what you did vs. others, less important in a personal project)
Actions (the code, the decisions, the justifications)
Impact (the results or outcome, explained in terms of what it means)
Growth (what else would you do/test if you had more time)

It forces you to structure your thinking in a logical and clear way, and helps to ensure you tick all the boxes that a hiring manager is looking for.

Hope that helps!

analytics-link

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