Even before the pandemic started, corporations like Stripe, IBM, Bank of America, and the Google subsidiary Kaggle hired remote data scientists. Many data scientists were already working in dispersed teams when Covid-19 closed offices and transformed other white-collar staff members into remote employees.
Data science is a remote-friendly industry and the profession of data scientists is one of the most flexible professions for work from home. Companies are eager to invest in the finest personnel, regardless of location, for jobs like data scientist, data analyst, data architect, machine learning engineer, data mining engineer, and data engineer. However, hiring data scientists remotely can be a difficult task.
While there are many freelance websites for hiring data scientists, it is difficult to gauge the skill set of freelance data scientists on independent platforms. The process of sourcing, vetting, and finding the right candidate can be overwhelming. To make your job easier, we help you understand how to hire data scientists. Hence, in this article, we discuss the top skills that every data scientist should have:
Table of Contents
1) Creativity and critical thinking
In day-to-day data science work, tasks are frequently ambiguous — at least at the start of the project. To get any value out of a data science solution, the data scientist almost always needs a lot of domain expertise.
For example, how can you construct credit risk models if you have no prior experience with the subject? Sure, you may do your best and adhere to well-known data science concepts, but it would only get you a little far. As a consequence, your models will not perform optimally, and your work will be dangling in the middle.
That’s where critical thinking and creativity come into play. Data scientists must distill a large amount of data in a short amount of time. Having a group of extremely creative people may reveal answers that no one else has considered.
Hence, if you are looking to hire data scientists, critical thinking and creativity are two of the most valuable workplace skills.
2) Mathematics and Statistics
When learning to create sentences, you must be familiar with grammar to construct proper sentences. Similarly in statistics, it is necessary to produce high-quality models. Data Science and Machine Learning begin with statistics. Even the notion of linear regression is a statistical analysis concept that has been around for a long time.
Every data scientist should understand the concepts of descriptive statistics such as mean, median, mode, variance, and standard deviation. Then there are probability distributions, sample and population, CLT, skewness and kurtosis, and inferential statistics, such as hypothesis testing and confidence intervals. If a data scientist has built a strong base on these concepts, their contributions will improve your team significantly. So, ensure you hire data scientists who have a strong foundation in Statistics and Mathematics.
3) Programming
It’s beneficial to learn certain code-related workflow skills that can help to perform better in the actual world. Working knowledge of Git and GitHub is required—these are tools for storing and managing multiple versions of code as well as collaborating with other programmers.
It isn’t technically necessary to know how to use the UNIX command line (also known as terminal, bash, and so on), but it can help to work more effectively by speeding up activities like text file processing. Working with cloud data requires command-line skills, and they may make it simple to automate normally time-consuming tasks like setting up a new teammate’s system with all of the tools and access they require.
Some of the widely used languages in data science are Python, SQL, and R. So it’s always good if you hire data scientists who know more languages, but it is essential for them to know these three.
4) Data Manipulation
Data wrangling and analysis determine the quality of a Machine Learning project. Data wrangling is the process of cleaning and transforming data into a format that is convenient to use in the upcoming phases.
Data cleaning is an essential skill for anybody interested in working with data. Data cleaning is adjusting formatting, removing errors, and removing duplicate items from an existing data collection to prepare it for analysis.
Although data cleaning isn’t everyone’s favorite aspect of the work, it is necessary.
The other necessary skill is working with Unstructured data. Unstructured data is any data that isn’t presented to you as part of a pre-existing data collection and isn’t well-structured. Unstructured data is, for example, streaming data from social media—a raw, real-time feed of everything submitted to a site. To analyze a data set, data scientists must develop the code that filters, sorts, and categorizes it, and this is a talent that almost every employer values.
5) Data Visualization
A Data Visualization specialist understands how to use graphics to tell a story. They should be comfortable with histograms, bar charts, and pie charts before moving on to more complex charts such as waterfall charts, thermometer charts, and so on. During the exploratory data analysis stage, these graphs are quite useful. Tableau offers a smooth interface with drag-and-drop functionality and offers a great set of libraries for advanced charts.
6) Machine Learning
It is a must-have skill for any data scientist. Just like AI is transforming the recruitment process, data scientists are also using machine learning algorithms to predict the achievable metric for the next month by looking at the past month’s data. Knowing the code for these algorithms (which is only a couple of lines long) is useful, but understanding how they operate is more beneficial.
Data scientists begin with basic linear and logistic regression models before progressing to complex ensemble models such as Random Forest, XGBoost, CatBoost, and others. This aids hyperparameter adjustment and, as a result, a model with a low error rate.
7) Deep Learning
Inspired by smart assistants, the interesting self-driving vehicle segment, or possibly the hilarious deepfake videos?
Deep Learning has made all of this feasible. Because of advances in data storage capacities and computing innovation, it is a high-growth area in the field of Artificial Intelligence.
To succeed in this sector, one needs to have a strong understanding of programming (ideally Python) as well as linear algebra and mathematics.
Data scientists should be able to develop both simple and complex models, such as CNN, RNN, and others. TensorFlow, Keras, and PyTorch are all essential libraries to know for data scientists.
8) Big Data
The advent of the internet, social media networks and IoT has resulted in a rapid increase in the amount of data we generate. The volume, velocity, and veracity of this data are all high, forming the 3V’s of Big Data.
Organizations have been overwhelmed by such enormous volumes of data, and they are attempting to deal with it by fast embracing Big Data Technology so that it can be properly stored and used when needed.
Before you hire data scientists, make sure they are well versed with frameworks like Hadoop, Spark, Apache Storm, Flink, and Hive.
9) Communication skills
When it comes to data science abilities, soft skills such as communication are sometimes disregarded. However, this may be the most crucial ability for data work. After all, even the finest analysis in the world won’t help unless you can make others comprehend it and persuade them to take action based on it.
Communication abilities, both written and oral, are very vital. Data scientists are frequently asked to provide reports or give presentations about their work. They also frequently work with colleagues who hold both technical and non-technical positions.
10) Domain Knowledge
If you hire data scientists, they should have a fundamental grasp of the business or sector in which they are applying. They should be able to comprehend the issue from the company’s point of view, transform it into a data science challenge, and address it utilizing the skill sets outlined above. Finally, they should be able to successfully explain the solution’s insights. However, keep in mind that the degree of the business or domain knowledge will be determined by the candidates’ experience.
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