Bringing data science, visualization, and reporting to everyone
Our two main products are Displayr, the world's first complete data science tool; and Q, the complete toolkit for market researchers. Our core business is a combination of advanced software engineering and data science, coupled with elegant design.
We currently have offices in Sydney, Chicago, and London.
Are you passionate about building secure, robust, scalable solutions that deliver value to the customer? This might be just the role for you!
We are looking for a Researcher who can both sell our amazing software, as well as demonstrate to our prospective customers how ground-breaking our products truly are. If you are a Market Researcher looking for a way to utilize your skills in a completely different way, this could be the role for you!
Our mission is to make it easier for everyone to discover and share the stories in their data. Our Engineering team is at the heart of this mission, and we need YOU!
For me it’s the flexible work environment, great pay, good engineering practices, and a high degree of autonomy that makes Displayr the best place I’ve ever worked.
MEGAN LIANG, CORE ENGINEERING
Our vision is to live in a world where evidence, rather than guesses and assumptions, drives decision-making. We work together to bring that vision to life and make it easy for anyone to discover and share the stories in their data.
We win by focusing on improvement. We develop people. We improve systems. We improve our product. We improve our clients.
When ownership is given, we take it. When ownership is not given, we take it anyway. If we fail, we proudly own that too.
Ship something to customers every day
We make a difference to our customers every day ("ship" is engineering speak for releasing features and bug fixes). We break our work into small, achievable tasks which we can finish each day.
Excellence is the next 5 minutes
Excellence is about what we do right now. It's not about creating a plan to be excellent in the future. It's not about perfectionism. It's about making a difference right now.
How we prioritize: Anecdotes < Instinct < Experience < Data < Logic
We prefer evidence over intuition, experience, and "best practice". We know that when data and logic differ, it's usually a sign that there's a problem with the data. We work hard to make the best idea win, not the idea of the most experienced person.
How we improve: Experiment > Document > Checklist > Automate > Optimize
Try stuff to see what works. If it works, document so that others can do it. Make the success easier to replicate by creating a checklist. Improve efficiency by automating. Then, lastly, optimize.
Minimize interruptions while being an open book
We minimize the number of meetings. We don't CC people on stuff they don't need to read. There are minimal broadcasts of generic information. But, the flipside is that only information we hide is information that we are legally obliged to hide. If you want to know something, it's pretty easy to find it out.