According to research by Gartner, companies consider poor data quality to be responsible for an average of $15 million in lost earnings per year. This means companies must derive value from their data which makes hiring a productive data engineer imperative in today’s cut-throat business environment.
For the third year, Data Science has claimed the most sought-after job role by Glassdoor and LinkedIn’s Emerging Jobs Report. The report also records Data Science as the fastest-growing job category, with more than 11 million job openings by 2026.
Thus hiring a productive data engineer is the need of the hour for most companies.
In this article, we will break down the meaning of data science and data scientist skills and tell you how to hire the best candidate for a data science position.
According to market research company Forrester, by 2021, insight-driven businesses will be collectively worth $1.8 trillion, up from $333 billion in the year 2015. These ‘insights’ are derived from data, which plays a pivotal role in helping the world’s most successful companies become more profitable. The same report found that data-driven organizations are growing 8x faster than the global GDP.
The ability to interpret data and harness its usefulness is clearly a pretty serious job. Moreover, due to the field’s scalability, it’s receiving the attention it demands. But without properly understanding what it is, how are we supposed to hire for it?
WeCP’s got you covered on both fronts.
In its simplest form, data science is the discipline of making data useful. The concept of data science is ‘to unify statistics, data analysis, machine learning, and their related methods’ to ‘understand and analyze actual phenomena with data.
Traditionally, the data we could evaluate was mostly structured and small in size and able to be analyzed by using simple BI tools. However, unlike data in the traditional systems, which were mostly structured, today, most of the data is unstructured or semi-structured. This demand has accelerated the role of a data science engineer.
A popular Twitter definition has described a data scientist as ‘someone who is better at statistics than any software engineer and better at software engineering than any statistician.’
A data scientist should be setting the company’s data strategy, which involves setting everything up from the engineering and infrastructure for collecting data and logging to privacy concerns. They decide what data will be user-facing, how data will be used to make decisions, and how it will be built back into the product. They will also be concerned with patenting innovative solutions and setting research goals. A list of their basic responsibilities includes:
Well-qualified data science engineers can be hard to find and even harder to hire. But, if you want to hire data science engineers swiftly — fear not. We have collected some of the best websites, services, and recruitment marketplaces ready to help you find and hire top data scientists.
|Upwork||Upwork is a good option for companies or startups looking to fill an immediate contractor role. However, if you are looking for a freelancer and have the time to select candidates yourself, you can consider hiring freelance data science engineers on Upwork.|
|AngelList||AngelList job is a resource for companies of all sizes. It also has a job board to help you connect with data science engineers interested in working with a startup.|
|SimplyHired||SimplyHired is a highly-respected job board worth investigating. With minimum effort, you can quickly post within a network of over a hundred job boards and browse through jobs by cities to see if posting a job listing on SimplyHired is worth your time and money.|
|Dice||Dice is an excellent tech and IT career board attracting software architects, engineers, QA testers, and data science engineers. Of course, posting a single job listing will cost you a few hundred dollars, so it’s a bit pricey, but the cost is well worth the possible exposure.|
|GitHub Jobs||GitHub is one of the most popular code repositories, attracting millions of independent programmers with in-demand skill sets. GitHub Jobs can be a great place to begin your search for dedicated data scientists and machine learning engineers.|
|Scalable Path||If you are looking to hire a team of data science engineers in a short time, then Scalable Path might be your go-to service. They offer experienced data scientists who are well-qualified to help you with your latest project.|
|Kaggle||Kaggle’s job board is one of the best sites to begin your search for top data science engineers and is utilized by some of the largest companies (Amazon, Capital One, and AIG — just to name a few).|
|iCrunchData is a data science job board that caters to employers seeking top data science engineers for hire. You can easily use this site to begin your search for data engineers.|
|Toptal||Toptal is one of the premier sources for hiring, with an acceptance rate of only the top 3% of the freelance data science engineers. If you’re open to hiring remote data scientists to collaborate with, then Toptal is a clear choice for your company.|
Your search for the best data scientist for your company depends on a captivating job description and a well-written summary of the job position. While much of it highlights the qualifications, duties, and objectives, a significant portion should convey your company culture and how the candidate’s participation will impact it. Don’t forget to keep the job-description short, sweet, and jargon-free whenever possible, and you’ll ensure that quality candidates will click, read, and respond.
To find your next hire, you can also write great job descriptions using ready-to-use templates from platforms like Monster, Toptal, LinkedIn Talent Solutions, UpWork, ZipRecruiter, Snaphunt, and Workable Resources.
Data Engineering roles enable data users across the organization with clean, quality data they can trust. This can drive better business insights and strategic actions. However, this also means that the role comes with a responsibility to understand the organization’s data infrastructure and tailor a solution for it.
It is imperative to say that there is a non-exhaustive set of skills that a talented data engineer must inculcate to keep up with the evolving demand. This fact, however, makes it hard to identify a productive data engineering talent that can fit into your desired role.
There are four broad categories to assess the competency of any data engineering talent. They are as follows:
New tools and best practices are constantly emerging, and the best engineers will stay abreast of the latest developments. However, classics like ETL, Data Warehousing, Data Structures and Algorithms, Data APIs, and Machine Learning are the building blocks for any data science engineer.
WeCP provides custom-made MCQs created by experts that can be used instantly.
MCQs are ideal for testing these fundamentals. They not only fit through the time limit of the screening test but also, most of the concepts can be checked theoretically. Moreover, this ensures no bias towards a particular stack or technology.
Data engineers build an infrastructure to keep data organized. This IT role requires a significant set of technical skills, including a deep knowledge of SQL database design and multiple programming languages like Python, and C++ for quickly computing large data sets, Java for writing APIs, Scala for its usage in Spark, and R to handle and analyze data.
A Programming question on the WeCP platform to test Data Science proficiency.
We can test these skills using Programming tasks and code gaps.
Any data engineer worth his salt knows that tools like Hive, Hadoop, Spark, AWS Redshift, and Apache Kafka are critical for their role. Unfortunately, we can not assess these tools via simple coding tasks as they contain proprietary technologies. Instead, the best way to test these skills is through MCQs and Code gaps. This saves time and resources and leads to a more efficient evaluation of tools-specific skills.
Data engineers should also have stellar soft skills like strong listening, effective communication, critical thinking, and adaptability. These help understand the candidate’s persona and if they would be a good fit for the company’s culture.
We can measure these skills through MCQs, a short interview, or a psychometric questionnaire.
According to a survey conducted by Glassdoor, the national average salary for a Data Scientist is $1,17,212 in the United States.
The salary of a Data Science engineer depends on the following factors –
Experience – People experienced in data science and engineering have a higher pay scale than others with lesser experience.
Academic achievement – Data Scientists with Ph.D.’s earn more on average than those with just a Bachelors’ degree.
Company size – The salary of a Data Scientist also depends on the organization’s size. Though many startups hire Data Scientists at competitive wages, there are a lot of smaller startups that pay lesser than the industry average.
You’ll compete against many great companies for the best candidates with these qualities. So here are a few tips to help you identify the right data scientists and successfully bring them to your organization.
“Great recruitment funnel” is the keyword here. It’s not hard to believe that one may have to filter through hundreds of applications to find a suitable candidate for a data science position. Therefore, it requires a lot more effort than just putting up a job listing and doing a few interviews to get it right.
You’ll need an effective recruitment funnel that:
Data science is a small, fast-paced world. One good way to meet and hire potential candidates you wouldn’t otherwise find is networking. Interacting with individuals from other companies will offer you invaluable insights into the current trends in the field.
Remember, the best data scientists are in high demand. But unfortunately, data science is so competitive that you have to find the right candidate and onboard them in your firm. So, remember that your whole recruitment process needs to double as an advertisement for how great your company is.
The tests, interviews, and other parts of your process should mirror how candidates would work at your firm. (If that’s not a good advertisement, maybe your firm needs a culture shift!)
WeCP’s coding assessments platform can help you streamline your Data Science recruitment process.
We are leaving you with a sample test template to hire a productive candidate for your data science requirements. You can try your hands on our data science coding test template below.
|Coding Test for Data Science|
|Logic Coding||Programming (Algorithms)||Python 2, Python 3, R||15-20 mins|
|SQL||Database||Joins, Clauses, Basic Queries, Nested Queries, Aggregate Functions||5-10 mins|
|Data Preprocessing, Data Visualization, Statistical Analysis, Model Selection||Data Science||NumPy, SciPy, Pandas, Matplotlib, Statsmodel, Seaborn, Sci-kit Learn||20-30 mins|
|Machine Learning Predictive Modelling||Machine Learning||Evaluation of model based on MSE, F1 Score, Precision, Recall, Accuracy, R square, MAE, MSE, RMSE||20-30 mins|
|Total Time||60-90 mins|
WeCP is a tech recruitment platform for conducting technical screenings and remote interviews. Our platform reduces the stress, cost, and time on the in-house technical team to recruit a viable candidate through:
With a repository of over a million questions for assessing over 2000 tech skills, with specific questions for Data Science engineering, WeCP helps recruiters analyze the problem-solving capabilities and expertise of the candidates.
Data Science Question library on WeCP platform
For a successful analysis of the candidate’s performance, an efficient technical screening tool filtering the qualified candidates with 1000+ programming assessment tests and 12+ metrics to hire tech talent with a pre-built and customizable library of tests makes the job of the recruiters and hiring managers easier and smoother by ten times.
Our platform analyzes candidates’ performance and provides detailed reports that are easy to understand for non-technical recruiters.
Understand the candidate’s test performance at a glance
Skill-wise bucketing of candidate’s performance for each skill.
Our Data Science engineering test template is intricately designed and includes questions that cater to your company’s demands and guarantee to help you find the best talent.
Use our pre-made test templates on Data Science to conduct live tests stress-free
That brings you to the end of our guide on how to hire an ideal candidate. There is a myriad of skills and each role requires a combination of different skills. If you know how to hire candidates the right way, you will always spot productive talent very easily.
Get in touch with us for more information because we are here to help you with all your technical hiring needs. We have got it all, from technical skills assessment (MCQ-based and simulation-based) to technical hiring (pair programming and interviews).