Soft Skills Will Make or Break You as a Data Scientist
As businesses gather an increasing amount of data related to various aspects of their organisation (e.g. internal business operations, customer purchases and behaviour), the demand for data-savvy employees has exploded over the last 5 years.
Business leaders have woken up to the fact that data-driven decision-making can lead to making better decisions (it is not the only factor of course, but that’s a discussion for another post). As a result, there is a strong demand for data analysts and data scientists across a wide range of industries.
Since HBR’s declaration that Data Scientist is the “Sexiest Job of the 21st Century” back in 2012, a plethora of online and university courses have flourished to allow interested students to learn the fundamentals of Data Science. However, there are several key aspects that deserve more attention than they get in the current discussion to ensure your long-term success as a Data Scientist. In this post, I will focus on two:
1. There is no such thing as a “typical” Data Scientist experience.
Your journey and work experience as a Data Scientist will massively vary depending on the culture and data maturity level of the organisation you work at.
You may spend the first few weeks or months mostly working as a Data Engineer setting up databases, and determining which data infrastructure would be most suitable for the organisation.
After this initial stage, then you may mostly worked as a Data Analyst spending your days writing Python scripts and SQL queries to organise, clean the various datasets collected, and set up automated data pipelines to automate data collection, cleaning, and processing.
After the foundations are built, then will you I start analysing structured and unstructured data to identify trends and patterns using statistical and ML models. Finally, you’ll be able to work as a Data Scientist and built various predictive, classification, and forecasting models.
Now, being able to get to this final stage can take a lot of negotiation and convincing, primarily to buy time to set things up before being able to provide valuable action-oriented insights. The truth is that all the hype surrounding AI is the best and worst thing that’s happened to it.
All the hype surrounding Machine/Deep Learning is the best and worst thing that’s happened to it.
It has increased interest (and funding) in the field but it has also created unreasonable expectations when applied in a business context. It has reduced the work that we do as Expert Statisticians with Strong Programming Skills (isn’t that what a Data Scientist is?) to a string of buzzwords and headlines.
This has created an environment where businesses expect outcomes right away. This is particularly the case if they don’t have an existing data infrastructure as they would often not know the preliminary groundwork that it takes, and I don’t blame them: you don’t know what you don’t know — that’s why you hire a data-savvy employee. Which brings me to my second point.
2. Your soft skills will make or break you as a Data Scientist
Your ability to communicate the value and insights that can be derived from your work is key to your long-term success as a Data Scientist. You need to get C-level staff on board as you need data-driven leadership. Your skills in presenting yourself and in articulating the value of your work will help you in hiring new employees and build your data-driven team.
Slowly, the results you are contributing to coupled with your ability to articulate the process and value of your work, will snowball and create interest in the rest of the organisation. Take this opportunity to up-skill current employees who are interested in learning more, and create allies of your Data Science team in other departments of the organisation.
Written and oral communication skills such as the ability to prepare progress reports, presentations, and interactive data dashboards to communicate findings and insights to key stakeholders will increase the actual and perceived value you provide to the business.
You must be able to translate your findings into business decisions by also providing brief non-technical background on the techniques you used and the biases and uncertainties inherent to the data science process.
Ultimately, in a business context, the best algorithm is not the one with the highest AUC score, it is the one that the stakeholders understand and trust enough to agree to use it effectively.
Building these soft skills alongside strong technical skills is no trivial feat and this is why skilled data scientists are so hard to find and so much sought-after.
But getting the skills to be a full-stack data scientist with strong communication skills is something that can be developed over time and should not be dismissed as optional.
