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How To Hire Your Next Data Scientist

Charanpreet Singh

Feb 3, 2021

Introduction

Do you think that a data scientist has a place in the design and development world?

With the data generated with every single activity on the web, it is imperative to understand data and get a better picture.

Organizations of every scale, type, and industry need and use data to refine their product ideas, design, and development.

A data scientist then becomes a crucial human resource to any organization as they can understand the number’s language and draw meaningful insights.

You can expect a data scientist to be adept in the following fields –

  • Machine learning
  • Software engineering or coding
  • Industry-specific expertise

Before hiring a data scientist, it is crucial to understand the potential data scientist’s responsibilities specific to your organization.

This is important because when it comes to understanding data, many noises come from the cohorts. The right person has the appropriate frequency to listen to the dynamics and develop readable conclusions.

To simplify, let us divide our discussion into four parts –

  • What to do before hiring a Data Scientist?
  • Creating a Data Scientist’s profile for your organization
  • Conducting interviews and assessment tests
  • An easy and flawless solution for all your needs pertinent to hiring a data scientist.

What to do before hiring a Data Scientist?

It is possible that not everybody on your team is as enthusiastic to onboard a data scientist like you.

So, the first thing is bringing everybody on board with this decision.

The reason is that a data scientist can influence the organizational decision down to its core, especially the operational processes of the product or service development.

Before hiring, check the areas where you need the assistance of a data scientist.

There are three broad areas –

  • Solving Problems of Optimization
  • Giving Recommendations
  • Sharing Business Intelligence

Firstly, you need to create a draft of the current operational issues you might be facing. The problems might differ from organization to organization or project to project. But as an example, a logistics company troubled with higher delivery costs can use a data scientist's help to frame better operational practices, leading to cost reduction.  

A data scientist can give recommendations in any form. Every organization needs to deliver its service and product while ensuring complete customer satisfaction. Now, to achieve that satisfaction level, you need the insights about a customer and how they react to your product and service offerings. A data scientist can provide such recommendations that allow molding your services and products according to consumer behavior.

Lastly, business intelligence (BI) provides insights for data management and arrangement. BI is about staying connected to the organizational operations in their entirety. It includes getting a sense of the KPIs, growth prospects, sales, revenue, customer acquisition, costs, expenses, etc. Understanding these statistics and the way they shape your organization can help you frame better processes.

Everything begins with problem specifications. Based on this, we can identify the type of data required to understand the problem. Then we have the methodology followed by proof of concept and validation.

Once the solution is validated, implementation can begin. Further maintenance, improvisation, and iteration are also a part of this process.

Thus, while hiring a data scientist, you must measure the candidate’s skills and understanding of all these aspects.

What to look for in a candidate?

Irrespective of your industry, a data scientist should be adept in understanding and working with these concepts and technologies –

The thing is that if you were hiring a data engineer or a data researcher, things would have been easier. In both these cases, the skillset and the mechanism to judge the same is straightforward.

But a data scientist's work profile lies between business intelligence, mathematics or statistics, and programming.

So, you are looking for a person who is apt with –

  • Computer programming and development
  • Skills to deliver data insights based on the core principles of mathematics
  • And has business knowledge

That is why, apart from the skills listed above, a candidate with experience and knowledge in econometrics, time series analysis, natural language processing, and image recognition can be preferred over others.

Concept or Subject Matter

How does it help?

Statistical Understandings and Linear Algebra

This aspect is directly related to the understanding and comprehension of data. The candidate should be able to collect, analyze, and infer the data.

Machine Learning

Machine learning may be an automated process, but you need someone to work on these technologies like ML and big data to classify the data and create groupings. Furthermore, it is required from a data scientist to provide algorithms for machine learning peripherals so that they are able to supply authentic and specific data.

Data Mining

We are surrounded by data. Everything that we do is recorded somewhere and then used to provide a better experience. A data scientist should be able to make sense of all the collected data and then gather actionable insights from the same. They should have adept visualization skills and the ability to help others understand the findings.

Technical Skill Set

Apart from the operational skills, you also need a data scientist who can work with the latest programming technologies. This includes; - Languages like R, Scala, Java, SQL, C, C++, Python, etc. - Specific Libraries including Pandas, NumPy, OpenCV, and Matplotlib. - Lastly, to create data structures, they should know how to operate excel, tableau, or Hadoop.

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Interview and Assessment of a Data Scientist

In the three domains that we mentioned above, which are essential to hiring a data scientist, there are some specific skills that you should look for while taking interviews.

Starting with Mathematics or Statistics, ensure that you include questions about calculus, algebra, descriptive and inferential statistics, probability, and other similar concepts. It will allow you to rank the candidates based on their expertise to handle complex mathematical calculations.

Secondly, you need to look at the core aspects of machine learning or programming. It includes understanding the candidate's ability to work with;

  • Regression and Classification
  • K-means and K-nearest algorithms
  • Decision Trees
  • Variance-Bias Tradeoff
  • Neural Networks
  • Boosting and Bagging
  • Naive Bayes Algorithm
  • Random forest classifiers
  • Multi-class, Multi-Label, and Binary Classification

The motive of getting an idea about these concepts is to know whether the candidate can build data pipelines.

Whether in a digital or a non-digital format, you need the data collated and scrapped off with the best bits available for the reader. A well-built data pipeline is vital to enhance data security, understanding, and applicability.

Lastly, under business intelligence, you need a scientist who understands your business and processes. More importantly, the right person should identify the problem accurately and then make apt solutions. These solutions will be based on the metrics and insights gathered from the residential data.

Should you be looking at a candidate’s experience?

Yes, very much.

The reason being that you cannot expect a college graduate to work on all three dimensions of a data scientist. It takes time to become a jack of all trades.

While setting the interview tests and questions, for the verbal or personal interaction rounds, include the following aspects –

  • Situational analysis and understandings: You can ask the person what kind of data is required for x problem and a probable solution.
  • Pluck small data sets from large databases and create questions about these sets while asking the candidates.
  • Ask questions about business intelligence and how, as a data scientist, the candidate can turn the x problem into data science.

Now let’s come to the test. Taking a real-time test of the candidate is significant. The candidate must possess the right combination of skills and talent required by your organization. It includes the right mix of academic intelligence and practical knowledge.

Onboarding a candidate who is not adept in all the fields of data science is riskier and leads to wastage of time and resources.

There is software to assess the real-world skill evaluation of the candidates. Without the help of these platforms, it can take a long time to create a paper test. So, it is better to use pre-made software-based tests for assessments. While creating or selecting the tests, try to include problems containing the aspects of the three dimensions listed above.

If not this, make sure to include a mix of questions based on machine learning, business intelligence, and statistical problems. This way, you will get a better picture of the candidate’s ability to solve real-world problems.

The assessment tests must be self-explanatory, time-bound, contain general desensitized data, direct, and gradated (gradual increase in the difficulty level).

Nonetheless, you must remember –

  • Not to set very high expectations from the candidates. They do not need to tick all the checkboxes in your requirement list.
  • Be patient with the hiring process. You cannot magically get a data scientist. They are not easy to find!

How can Skuad help you hire a Data Scientist?

Skuad is a Global Employment Platform that helps companies onboard contractors, hire employees, manage invoices, and pay team members anywhere in the world.

You can hire full-time, part-time, contractual, and freelance data scientists from anywhere in the world without worrying about how to manage international talent.

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