You can easily find yourself set up for failure. Verifying and checking metrics before you start was one of the most valuable lessons I learned (the hard way).

Before starting any project to improve an existing model or process, be sure to check you’re not being measured unfairly (photo by William Warby on Unsplash)

Destined for failure

This is an issue that I’ve seen a few times over the years and have run afoul of early in my career. It’s also one of the most important lessons I ever learned.

It’s common knowledge that a large proportion of data science projects fail. This is often attributed to working models never making it into production and the difficulties around MLOPs and machine learning engineering. Before I continue, if you’re wanting a super simple guide to walking through the end-to-end process I wrote a post on this a while back that goes through the basics. …

Save money and frustration by considering the roles of analysts, data engineers, and software engineers in your data teams.

Stop hiring data scientists — you probably don’t need them (photo by Nadine Shaabana on Unsplash)

The Wrong People for the Job

I’ve seen this across many companies I’ve both worked in and consulted for throughout my career — we’re not hiring the right people for the job!

I still hear a lot of horror stories of data science teams underperforming, management losing confidence in data science in general, or data scientists getting frustrated in their roles. When you take a deeper look, you see a common pattern.

We keep hiring data scientists without really understanding where they bring the most value.

For many organisations, it’s analysts and data engineers that are really what’s needed. Sometimes, it’s not even data or insights…

Taking the leap from learning to application can be tough. Here are some things to look out for to smooth the transition.

Don’t spend all of your time in the classroom (photo by Shubham Sharan on Unsplash)

Beware the Bookworm

It shouldn’t surprise you to know that I — like many data scientists — am a bit of a bookworm. I love to read and try to pick up new skills. There’s a special feeling when you hear something that just clicks and makes you think about something in a completely different way.

One thing that’s bitten me again and again throughout the years, however, is that gap between reading how to do something and actually doing it. …

The most common reasons strong candidates get stuck in an interview are often easy to fix with the right focus

Images of a computer
Photo by Boitumelo Phetla on Unsplash


The tech world seems to have gone mad for data scientists in the last few years: many people want to get into the role and plenty of organisations are looking to hire them. As someone that’s interviewed and hired scores of talented, capable individuals for the ‘Sexiest Job of the 21st Century,’ I can tell you, it can be a painful and difficult process for both sides. Whether you’re a candidate trying to find the perfect role or an organisation seeking the right fit — there are plenty of pitfalls to watch out for.

For those getting into the role…

The soft skills that most developers struggle with and some guidance on how to improve them

Frustrated man using phone
Are soft skills dragging you down? (Photo courtesy of Reshot)

It’s Not All About the Tech

As techies, we can get really excited about learning the newest tools and approaches. There’s always some big discussion in the community, like R vs. Python, Power BI vs. Tableau, TensorFlow vs. PyTorch, etc. It’s easy to get drawn in. You’re passionate about tech. Good.

One thing that can really hold strong technical people back, however, is soft skills. Soft skills are hard because developing them is often uncomfortable and there’s no documentation.

In this article, I’m going to outline the top five soft skills that I think can make the biggest impact in any tech career.

1. Selling


A lack of available data is stifling the adoption of machine learning solutions. Federated Data Sharing might be the answer.

Many organisations are out in the cold when it comes to high-quality data (photo by Harrison Haines from Pexels)

From the earliest themes of artificial intelligence in Greek mythology, people have long thought about AI and the possibilities it may hold. With the advances in computation and mathematics, Alan Turing’s 1950 paper on thinking machines sparked the first real developments of this in practice. The first proof of concept was initialised through Allen Newell, Cliff Shaw, and Herbert Simon’s, Logic Theorist, a program designed to mimic the problem-solving skills of humans — considered by many to be the first artificial intelligence program presented in 1956.

AI is 60 years young, we’re only at the very beginning.

Despite its 60…

A look at why the ‘sexiest job of the 21st century’ has lost its appeal

Driven to tears — when dream jobs turn bad (photo by Andrea Bertozzini on Unsplash)

Dream job?

We’re often bombarded with how popular data science is as a career. It’s all too common to read things like data science being the “Sexiest Job of the 21st Century” or yearly comparisons of high salary expectations.

Data science has a lot to offer. It’s a challenging role with plenty to learn and keep you occupied. Compared to many other roles, data scientists can be given a lot of autonomy to explore and solve interesting problems. And, in many cases, you’ll get the opportunity to work with talented and skilled people in a variety of domains.

Despite this, many data…

Improving the #1 skill for data professionals

More than anything else, how you communicate will be the dominant factor in your success as a data scientist. Hear why and what you can do to turn communication into a strength.

Get comfortable drawing a crowd, what you have to say and how you say it matters (photo by Wan San Yip on Unsplash)

I get asked a lot by aspiring data scientist questions about how to improve and what they should be focusing on. Things like:

  • What framework should I learn next?
  • What models should I be using?
  • Should I learn Julia or Spark next?

While these are all valid things to be considering (in the right context) for the vast majority of people they’re not where you should be focusing…

Books that’ll give you a wider perspective when working in data science and applying machine learning at scale

Reading a book
Photo by Lars Poeck on Reshot.

I first started coding during an extended hospital stay back in 2010. I told myself, “If I’m stuck here, I want to learn something useful.” Like most people who take the plunge, I soon got carried away with this newfound power! Even the incredibly simple things I was able to do in C++ opened my eyes to the possibilities and wonder of it all.

I was first introduced to machine learning during my Ph.D. I was researching tools and techniques for optimisation in high-powered laser defence systems and stumbled upon reinforcement learning. …

Tips to help you stand out from the pack and get you hired from an experienced data science leader

Get hired! (photo by Sincerely Media on Unsplash)


Data science is a highly attractive career for many reasons — so the competition can be tough. Some of the tips below that make a great candidate stand out might surprise you!

I’ve written this article for a variety of audiences:

  • You might just be starting out and trying to land your first role
  • You could be in a tangential technical field and want to make the switch
  • Or you might already be an experienced data scientist looking to brush up on your skillset.

Why is it so hard?

This is a question I often get at user groups and community events for aspiring or…

Adam Sroka

Dr Adam Sroka, Head of Machine Learning Engineering at Origami Energy, is an experienced data and AI leader helping organisations unlock value from data.

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