We’re hiring a few backend developers, frontend developers, as well as data scientists (interns). The internship will start in January, last 4 months, and take place at our Markham, ON, Canada Software Lab location.

You must be enrolled in a Co-Op program at your university/college and be eligible to work in Canada to be considered for this position.

IBM internship positions

To apply, please use the following links:

• Backend Developer Intern
• Frontend Developer Intern
• Data Scientist Intern

You can apply for more than one position if you wish.

What to expect from our interview

Interviews are scary, amirite? Lucky for you, I perform the technical interview. I don’t ask brain teasers (i.e., how many useless interview questions are currently being asked in New York City?) or algorithmic questions.

I also do not ask you to solve anything on a virtual whiteboard, code anything on the fly, or expect you to remember specific details you can easily Google while performing your job.

Instead, we’ll have a technical conversation about your previous projects, and I will ask you more general questions that tend to reveal with greater accuracy your current understanding of software engineering (or data science).

For example, if you list REST APIs on your resume, I might ask you what’s the difference between 400s and 500s errors. I’m not going to ask you what a 413 error represents.

The most promising candidates will proceed to a second interview with me and my manager.

This second web interview has little to no technical questions and it’s just an opportunity to learn more about you and your ambitions, and for you to learn more about our team and ask questions.

At that point, we’ll decide who to hire. That’s it. No gruesome six rounds of interviews. Easy peasy.

This easy-going interviewing method has enabled us to hire great candidates with virtually no false positives.

What we value

It’s worth noting that we value cultural contribution over cultural fit. We don’t need clones of our existing team members, as excellent as they are. We need people who add something to the team and bring fresh new perspectives.

Also, don’t doubt yourself. If you are interested, apply. We value passion and attitude over raw experience. A bad hire is not someone who doesn’t know much but is willing to learn. A bad hire is someone who knows everything but can’t work in a team.

If you apply, feel free to email me your resume as well so that I can speed up the interview process.

Our interns tend to love the experience in the team and will often come back for another internship or as regular employees once they graduate.

Talk to you soon.

The post New IBM Internship Positions in My Team appeared first on Programming Zen.

However, technology changes quickly and so has my job over the years. I’m still a software developer and technical evangelist, but my team’s name, scope, and goals have evolved over the years to keep up with the rapidly changing technological landscape.

In recent times, my attention has been focused on two projects, The first, a tool for data scientists, will be discussed in this post, and I’ll introduce you to the second in next week’s post.

This first project is called Data Scientist Workbench. It’s an all-in-one solution for programmers, data engineers, data journalists, and data scientists who are interested in running their data analysis in the cloud. Oh, and it’s absolutely free, no catches or strings attached.

It includes Jupyter notebooks (often simply known as IPython notebooks amongst Python developers), Apache Zeppelin notebooks, a web version of RStudio IDE, OpenRefine to clean messy data before processing, and Apache Spark to handle large volumes of data.

Being a well-orchestrated collection of Open Source tools, your notebooks and data are not locked and can be exported for offline use whenever you want.

Python, R, and Scala are supported at the time of writing. Your workbench runs in a virtual machine which currently provides you with 100 GB of disk space, and 16 GB of RAM. This leads to processing that is faster than most people’s laptops. Thus you don’t have to mess your laptop up, slow it down, or spend hours installing anything. Instead, everything can be handled conveniently from a browser, from anywhere you find yourself.

Speaking of adding things, many libraries can be installed independently by you within the notebook through a command such as !pip install. If your library has OS dependencies that are not installed within your instance, you can ask our Toronto-based team for help. We are attentive and responsive to user requests and feedback, and will try to accommodate most reasonable requests in a timely manner.

On top of the usual features you might be familiar with, if you have played with, say, Jupyter notebooks, the Data Scientist Workbench adds a host of nice features such as drag and drop to upload of files, sharing of notebooks (including publishing them as Github Gists), the ability to import such links by just pasting them in the search box, and pre-created sample tutorials.

These tutorials can be imported with a single click, so that you can learn not only about DSWB (as we like to shorten it), but also familiarize yourself with the data science, big data, and machine learning technologies covered within.

So far the feedback I’ve received from people at meetups and data science hackathons I’ve been involved in has been universally positive. This is not to say that it’s perfect, of course, but it is a genuinely useful tool that is provided, again, entirely for free.

Above all, our small team launched it as a startup would. We created an MVP (Minimum Viable Product), and set it free, iteratively improving it quickly, incorporating user feedback, including both praise and complaints, as much as possible.

I think it’s a cool product that you’ll enjoy and I would like to personally invite you to try it out. Specifically:

1. Register here.
2. As you use the workbench, please give us feedback. Click on the question mark at the bottom of the page or click on one of the many options we provide for you to contact us (see sidebar, under Resources).

We have big plans for DSWB, so it’s important that we get as much user feedback as possible, and definitely part of the reason why I’m sharing this here with all of you. Stay tuned for next week’s post about my second focus project. Even better, subscribe via RSS feed or by email.

The post Data Scientist Workbench: IBM’s All-in-One Tool for Data Scientists appeared first on Programming Zen.

I share this as a way of illuminating the fact that there is a reward to be had if you enjoy keeping up with technology and the programming world – and it’s a payoff that goes hand-in-hand with the sheer fun of exploring and using new cool technologies.

We live in a world that moves quickly, with significant innovations being introduced every couple of years and entire tectonic shifts coming along every decade or so.

This doesn’t just apply to programming languages either – we’re talking databases (e.g., NoSQL), paradigms (e.g., parallelism and concurrency), new approaches (e.g., Big Data in the Cloud), new frameworks (e.g., Meteor), and many other corners of this vast field.

In the programming world, in order to stay competitive, you can not ever stop learning. If you’re not leading the pack, you apt to be left in the dust.[2]

One way to keep up is to never stop experimenting, playing with new languages and technologies in your spare time and applying them to non-mission critical projects. That’s the strategy I have grown accustom to at least, and again, no complaints from me when it comes to taking such an approach.

Wayne Gretzky, arguably – if not flat out – the best hockey player of all time, has a great quote that applies to hockey as much as it does to life in general.

I skate to where the puck is going to be, not where it has been.

Damn, Gretzky was good on the ice. Anyway, back to the topic. Some developers might consider R or Node.js to be the future. In reality, that’s where the puck is now.

Where will the puck be in three, five, or ten years? We’ll have to wait and see to know for sure. Meanwhile, I encourage you to explore three programming languages that might very well be headed in the same direction of that most iconic of Canadian sports equipment, the humble yet mighty, puck.

• GO: A fast, truly cross-platform, concurrent language that resembles a modern C on steroids.
• Julia: A very high level language that’s aiming to be the future of scientific computing and data science, including integrating with existing solutions such as Python scientific packages. Think of it like a modern Fortran on steroids.
• Elixir: This language fully leverages the power, reliability, and concurrent nature of the Erlang VM, but does so with an elegant Ruby-like syntax. It’s Ruby taking steroids in the gym she goes to seven times a week.

I make no promises or guarantees that these languages will pick up massive momentum over time. What I can promise you is that you’ll enjoy and learn from them in the moment. Each one is not only promising, but already delivering and is well worth checking out, if you haven’t already.

Let me know where the puck is headed for you.

The post 3 Upcoming Languages That Are Worth Exploring appeared first on Programming Zen.