12 Key Data Scientist Skills in 2021
What skills and tools are the most in-demand for data scientist positions? We wanted to refresh this important topic since the industry has evolved significantly over the past several years!Every company will have their own priorities when it comes to different skills, and this includes technical, industry knowledge, education, and intangible skills (like professional demeanor and communication). Also, technology is changing faster than ever before and new tools are cropping up all the time. Below are the updated skills and tools that are most highly requested for the data scientist positions we are currently recruiting for across our global client portfolio.
Technical Skills:
1. Advanced Degree
Data scientists are highly educated – in our 2020 report we found that 94% have at least a Master’s degree and 43% have PhDs – and while there are some exceptions, most employers still view a strong educational background as a requirement to develop the depth of knowledge necessary to be a data scientist. PhDs are heavily requested by employers looking to recruit data scientists to work with unstructured or streaming data, or some may require that a candidate with a Master’s degree have more work experience.The most common fields of study for our data scientist sample were Mathematics and Statistics (25%), followed closely by Engineering (24%), Computer Science (22%), or Natural Sciences such as Physics (16%). You can learn more about this in our data science & analytics salary report which can be downloaded here.
2. Python Coding
Python is the most common coding language we see required in data science jobs. SQL is usually considered a baseline skill to go along with that, and we don’t see many requests for R or SQL on their own. While some data science positions may have requested Java or C++ in the past, these are not as commonly requested in terms of data science must-haves now.In terms of Python libraries and other tools to be familiar with, Pandas, NumPy, and Scikit-learn are key for machine learning and model building. Tensorflow, Keras, and PyTorch are frameworks within Python for deep learning.
3. Experience with Unstructured or Streaming Data
It is critical that a data scientist be able to work with unstructured data, whether it is from social media, video feeds, sensor data or audio. While there are many positions under the analytics umbrella that may focus more on structured data, typically for data scientist positions, significant experience with unstructured or streaming data is a requirement.
4. AI: Machine Learning & Deep Learning
Machine learning and deep learning are now an integral part of all the data science jobs we recruit for, so the more familiar you are with these types of techniques, the more marketable your skills will be.
5. Cloud: AWS, Azure, and GCP
While Hadoop may have been highly in-demand in prior years, with everything moving to the cloud, we’re now seeing mostly requests for cloud tools like AWS, Azure, and GCP. Apache products, like Hadoop, Hive, and Spark, are continuing to decline.
6. Natural Language Processing
NLP involves teaching computers to understand, analyze, and respond to human language. One example is sentiment analysis, which uses the text of an article or post, etc. to determine whether the tone of the author is positive, negative, sarcastic, or otherwise based on context. There are many business use cases for NLP beyond this example, so it’s been a highly requested skill as more companies look for ways to leverage their data.
7. Business Intelligence Reporting & Visualization
While visualization and business intelligence skills (such as Tableau) may not always be listed as a requirement on a data scientist job posting, it’s generally considered part of the package to know how to present your work. Reporting or visualization may not be a huge focus of the job, but these skills are often considered a baseline requirement.
Non-Technical Skills
8. Intellectual Curiosity
This was a popular request for data scientist positions years ago – and it still is! Employers love data scientists who love to learn and are passionate about their work, and who are always looking for new questions to ask.
9. Business Acumen
To be a data scientist in a business role, you’ll need a solid understanding of the industry you’re working in and know what business problems your company is trying to solve. In terms of data science careers, this means that being able to discern which problems are important to solve for the business is critical, including identifying new ways the business should be leveraging its data.Having a strong understanding of the industry you’re working in, your company’s goals, and how your work supports those goals is key to improving this skill.
10. Communication Skills
Similar to requests for visualization skills, companies searching for a strong data scientist are looking for someone who can clearly and fluently translate their technical findings to a non-technical team, such as the Marketing or Sales departments. A data scientist must enable the business to make decisions by arming them with quantified insights, in addition to understanding the needs of their colleagues and other business units. For example, if the data scientist will be working in a Center of Excellence, they’ll be expected to work with lots of business stakeholders, and need to be able to talk and present to the different teams effectively.
11. Stay Updated on New Technologies and Trends
This one might seem like a no brainer, but with the field evolving so quickly, it can be hard to keep up with everything! As stated earlier, data science & analytics is constantly pushing forward, which means you will be constantly expected to learn new tools and techniques in order to stay at the forefront of this field. The multi-year analysis from our 2020 SAS, R, Python tools survey shows just how much tool preferences have changed over the past few years.
12. Consult with IT and Data Engineering
While a few years ago (and still in some cases) data scientists were expected to do all of the data wrangling and preparing the data, now many teams have data engineers working alongside data scientists so that the data is already in good shape for the data scientists to work on the analysis. This means that in addition to working with other business units, it’s also important to have a strong, collaborative relationship with data engineers and other IT professionals on the team.Interested in beefing up your skills, breaking into analytics or data science, or transitioning to a new role within these fields? Check out our guide here.
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