How to Hire Data Scientist

According to the annual Indeed report, 2019 will continue a 5-year tendency and show the total growth in demand for data scientists that will reach 29 percent.

6 min read, Aug 2

The foundation of the increase is that with digitalization business becomes even more driven by various types of data that should be processed, analyzed and forecasted. Additionally, traditional statistical techniques become obsolete. since they can’t handle the size and power of the informational flow as well as the diversity of its elements.

Therefore nowadays they are being replaced by approaches of data science that exists as an independent scope only since 2001. Combining statistics, analytics, machine learning and close fields of study its application became a necessity for companies that decided to step on the path to business optimization.

When do you need data scientists and what should they do

However, it doesn’t mean that enterprises will such attitude namely need to hire a data scientist. If talking about taking under control data flows it doesn’t start with that. Fill a gap in this position is required when an appropriate data infrastructure and supporting procedures are already established.

For the early stages of business development, a data engineer is sufficient. The one has lower salary expectations and can centralize data, set up and perform management, analytical and quality processes. Once it’s done more advanced analysis can be run by a data analyst.

It might sound that data scientists are needs only of the chosen ones and big players. But actually, it only excludes companies with not yet defined or suitable business models. It will be an excessive luxury that won’t bring the required results. As already noted it’s the most trended but not the only job description that includes database maintenance-related tasks.

Data scientist qualifications should commonly serve for resolution of real business problems and should be sufficient for performing the following responsibilities.

Insight acquirement

When needed a pro in data science company goals that should be achieved are already determined and will be presented during an interview and instruction. Candidates on their side should deeply comprehend them and add specifics that will help to form a concept, implement and test a solution for their attainment.

Data collection

It implies defining appropriate variables for tracking and data for gathering from the diverse sources whether it’s structured or not. It also includes further preparation for analysis by validation records to ensure its quality, comprehensiveness, precision, and relevance.

Model development

An applicant should be able to drive the full creational cycle to deliver practical statistical, analytical and predictive models for various purposes. While in some cases common approaches can be taken for the development the result should be custom and meet the company needs.

Value elicitation

The essence of the data scientist role description. Simply saying an employee should see business challenges and opportunities as well as their metrics and solutions behind numbers, their sequences and probability of appearance.

Workflow optimization

It concerns not only the improvement of the efficiency of the selected department performance but also monitoring and analysis of processes associated with data science. Considering their complexity this is the most appropriate way to define and bring in the required changes.

Result sharing

Drawing up and presenting concepts, reports, and solutions that are based on the research results are also make the list of data scientist responsibilities. Their accuracy, structure, utility, and delivery including verbal should be convincing enough for a company C-suite to induce changes in aimed aspects including a business model.

Such a wide range of tasks require much bigger knowledge basis than a statistician has. Practical implementation of models and solutions require advanced technical skills that should be proved before a particular talent acquisition.

Data scientist interview questions

Needed financial expenses for integrating a complex analytical unit and coming with them expectations put a pressure of responsibility on the HR department to find the best candidate or the right data science company to set up the process externally.

Of the obligational status of a technical interview data scientist position, applicants should be informed during the hiring procedure. It can be challenging for companies with low-tech culture but it’s a must-do for the job title of this level. The following list of skills on this side exists but it doesn’t mean that all its items should be in the resume of a candidate that will be able to meet the requirements.

Languages/frameworks. SQL, Python/PyTorch, Tensorflow, C, C++, Java, Julia, Scala, R, etc.

Specific software. MATLAB, MySQL, Hive, Spark, Hadoop, etc.

ML techniques. supervised/unsupervised, artificial neural networks, clustering, etc.

The question that might arise if for a data scientist machine learning skills are really needed. There are data and actual machine learning engineers and experts. But considering that it’s a coordinating position of enterprise analytics and its influence on strategic planning a candidate should have a tech toolkit and rшch background of using it.

Other topics that should make to technical interview questions for a data scientists concern theoretical basis and include statistical algorithms, probability, A/B testing techniques, big data and other areas that allow a candidate to show a quality performance with a note of creativity.

The best approach to detect skills under facts is not focusing on just simple can and what questions but asking to solve hypothetical problems. It will help to grasp not just an applicant’s technical baggage but the level of decision-making in the short-term condition.

Working experience must be also presented to find out achievements within specific domains. Data science for various scopes is varied drastically so if an interviewed person is changing fields than HR should ensure that he or she is familiar with the peculiarities of the new one.

Where to look for data scientists

This position doesn’t obligatory should be in-house for the best results. Analytics outsource is an appropriate option that allows cutting costs on management and operational aspects. Besides, it gives the ability to not focus just on a local labor market but find talents for your goals worldwide.

If judging by average salaries that reflect the state of local market development the top countries to look for professionals are the USA, the UK, Switzerland, and Germany. However, there are other regions respectable in the IT scope that can provide qualified specialists for companies with limited budgets.

Data science companies in Ukraine as other providers of digital solutions recommended themselves form the best side around the world. Partnerships with pros from this country of the East European market is associated with cost-efficiency thanks to lower rates for the high quality and efficient development that can be easily coordinated without unfavorable reshaping of the schedule of local staff.

Springs is a reliable partner that can provide experienced specialistы that will be able to fulfill the requirements of your company position of a data scientist Python/PyTorch, MySQL and so on expertise included. Representing the Ukrainian market that continues growing in numbers and proficiency Springs team contributes to raising its profile in the scope of data science.

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