Know 10 Things Companies Will Look for in Data Science Candidates in 2022

Here is the list of things that data science candidates must take into consideration for getting recruited

Data science is becoming a critical mission to more and more businesses. One of the biggest challenges in this mission is recruiting skilled data professionals. In tech companies, the demand is no longer confined to the high-tech and software realms. The significance of leveraging data science tools and techniques has boosted the demand for data professionals. Over the past couple of years, there has been massive growth in data science-based jobs in sectors like education, marketing, and manufacturing. This phenomenon has driven many data science aspirants to choose this domain as a career. This article lists the things that companies will look for in data science candidates in 2022.

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Must-Have Skills

Machine learning is no surprise as the most important skill to have for a data scientist. Data mining and Data analysis are the key activities that every data scientist has to go through. Strong statistical modeling is required to be a better data scientist. Companies are expecting a good knowledge of deep learning since it provides state-of-the-art techniques to solve some interesting real-time problems in fields like NLP and Computer Vision. Employers are expecting the candidates to know big data technologies due to the huge rise in the amount of data recorded every day. In real-time, companies might be working on huge datasets where these skills will come in handy.

Ability to translate to ML problems

Being good at engineering machine learning (ML) algorithms is one thing. Being good at understanding business problems is another thing. But merging those two and figuring out how to solve business problems with ML is a whole other deal. You need to be able to translate real-world problems into machine learning problems that you can solve.

Automation and Optimization

Everybody hates repetitive tasks. Some people hate it so much that they do whatever they can to automate it. It is about everything from buzzwordy things such as autoML and GitHub co-pilot, to automating the setup of the code environment and generally everything-as-code, to even automating daily time registration, etc. Automation and optimization are some of the hallmark mindsets of great developers/data scientists.

Passion & Curiosity

Passion and curiosity are qualities that are desirable for anyone working with technology. Data science being the great beast that it is, it is an even more ubiquitous prerequisite in this specific field. In many other technical fields, you can specialize in a set of skills and use these to drive business value for years on end, perhaps with the need to learn a new programming language or tool every X years. Data science, however, is inherently a scientific discipline that is developing daily.

Coding Knowledge

If you are starting to learn Data Science, In the beginning, you'll find it hard to choose the right programming language. Though there are many languages, the competition has always been among Python and R itself. The industry is still in favor of Python due to its rich libraries followed by the R language. SQL is a must for every data scientist. Though it doesn't fit to be treated as a programming language I still included it here by taking my chances. After python and R there seems to be good demand for SAS and C++ languages.

Pragmatic and Value Seeking

Data science is a scientific discipline. However, when you get employed as a data scientist, the job is usually about applying data science tools to create business value. Rarely is it about doing research, coming up with new algorithms, breaking new ground, etc.,

Background

Having a background in bioinformatics, quantum physics, or other scientific fields is advantageous when venturing into data science; it means you are used to reading research papers, have done statistical analyses before, maybe a bit of programming, etc. Having a fancy education, however, is by no means a requirement. It is just a few years of structured learning. But naturally, what you have done and achieved previously is considered when applying for new jobs.

Experience

The last point on my list is actual data science experience. Naturally, it is advantageous if the candidate has been exposed to various disciplines within the field; working with computer vision, natural language processing, forecasting, classic supervised/unsupervised techniques, general deep learning, etc.