Want to be a great data scientist? Think like a translator
When I started working with data, I realised that my job has a lot in common with translation. I translate the language of technology to the language of business.
I was surprised but delighted. What better way to see the whole picture of a company and its product? To operate in two different worlds but understand both sides?
I found it fascinating. Thinking about my work as a kind of translation pushes me to think more broadly, to focus on flexibility and openness.
But, of course, it’s a challenge too.
So, to help me frame the approach, I looked at the 8 traits of a great translator and then translated them into the reality of working as a data scientist. You won’t believe how similar they are!
Here is my list of the translation skills that you need to be a great data scientist. I hope it will give you some helpful insights into your work or the work of your colleagues.
So, let’s start:
A data scientist must speak the languages of both technology and business. It’s not enough to be native in one and only conversational in the other. If you’re going to harness the power of technology to deliver results for a business, you need to speak both languages like a native.
When you talk to engineers, be prepared to get into the nitty gritty of systems architecture and events. When you talk to product managers and business analysts, be fluent in the language of roadmaps, CPA and ROI. When you present to the CEO, synthesise all these acronyms and jargon into plain English that is easy to digest and understand on a tight schedule.
Appreciation for other cultures
If you can’t appreciate the different cultures of business and technology, it’s difficult to be a good data scientist.
“The best translators are determined to break down the misconceptions, stigmas, and other barriers that prevent various cultural groups from understanding and embracing one another.”
On the one hand you have complex tech systems with many variables and interpretations. On the other you have a business need for a simple, yes/no answer.
Somehow you have to simplify the complexity into a usable answer for the business, while ensuring that your conclusion is accurate and stands up to interrogation.
Sometimes it can sound like mission impossible. It’s hard but if you appreciate, respect and understand both sides, you will find a way.
Awareness of the evolution of language
“Vernacular is constantly changing.”
This quote is particularly true for the language of technology. Few industries are developing and changing as fast. A tech stack is a living organism, constantly evolving and changing as agile teams press and mould it into different shapes.
You need to be aware of that, to understand why your company is building a new microservice instead of adding to a monolith. If these terms don’t make sense to you, learn about them. It will pay dividends in the long run.
Area of specialisation
General awareness or business and technology is important, but a specialism will set you apart. In smaller companies it’s easy, indeed necessary, to be a generalist. But as companies grow you can carve out a domain in which you’re the expert.
Perhaps it’s customer behaviour on mobile apps, or forecasting the revenue with it’s seasonality, trends and changing environment.. A niche will help you stay particularly close to one part of the business while translating that knowledge into your duties in other areas. You will become a valuable source of knowledge rather than just a go-between.
Attention to detail
“When it comes to professional translation, no detail is too small. A single word, letter, or even accent can alter the entire meaning of a document.”
This truth is self-evident to any data scientist. When you’re dealing with data, no question is too small, no detail insignificant. A detail such as misunderstanding what is triggering the event can change the entire narrative. It’s always better to be safe than sorry.
Ability to accept criticism
Working between two worlds often makes you a magnet for criticism. The trick is to understand this as one of the best parts of the job. Constructive feedback is one of the fastest, most effortless ways to learn. And when the feedback isn’t constructive, try not to take it personally. You can’t please all the people all the time, and you’re doing your best to be helpful.
Time management skills
This one made me laugh because it’s so accurate. Yes, “tight deadlines are standard” and, yes, you need to “know how to strike the perfect balance between speed and accuracy.”
Part of your work as a data scientist is to react to a fast-changing environment, making rapid decisions and about what can and can’t be done. You will be bombarded with ad hoc questions as well as your regular workload, all the while thinking about the future and creating processes to improve your workflow. It’s a challenge!
Passion for language
This one barely requires comment. Being a data scientist is a challenge and requires constant learning. If you don’t enjoy it, it might not be worth it.
A final Note
I hope that you enjoyed this article. If you work in data, I hope that it provides a useful perspective. If you are a translator, perhaps you can consider a career in data science! If you are my friend, thank you for reading to the end ;-).
Data science is a great field to work in if you like challenges, love to learn and relish exploration. It’s a career for the curious, and I love it.