How to hire a data scientist

June 27, 2019
7 mins

Data science is one of the most sought after jobs of the 21st century. But how do you hire a data scientist who fits the bill?

According to Firstround.com, in a competitive field like data science, strong candidates often receive 3 or more offers, so success rates of hiring are commonly below 50%. The key is to have prospective candidates go through the recruiting process quickly, helping close data science positions faster. This is possible only if the right objective is set before the hiring starts. 

Finding the right candidate for the Data Science role

Finding the right candidate for the role of a Data Scientist can be tricky and challenging. This article will help you understand what Data Science is and what skill sets to look for in a candidate when hiring for a Data Science role. 

Data Science

Data science is an interdisciplinary field that uses a blend of data inference and algorithm development to solve complex analytical problems. An ideal candidate has skills in the 3 fields: mathematics/ statistics/ machine learning/ programming and business/ domain knowledge. 

Skills required for a Data Scientist

Mathematics and Statistics 

A candidate applying for a data science position should have a good understanding of certain mathematical concepts. This includes topics like statistics (both descriptive and inferential), linear algebra, probability and differential calculus. 

Machine Learning and Programming

Any candidate applying for the Data Science role should have strong programming skills. One should have a good understanding of basic programming concepts, data structures such as trees and graphs and most commonly used algorithms. The candidate should be able to code in either of the languages – Python or  R-which are the most widely used languages in data science in the industry.

Apart from the programming skills, the candidate should have a good understanding of machine learning concepts like:

  • Classification and Regression
  • Supervised Learning and Unsupervised Learning
  • Clustering Algorithms line K-Means, K-Nearest Neighbors 
  • Decision Trees and Random Forest classifiers
  • Naive Bayes Algorithm
  • Boosting and Bagging
  • Bias – Variance Tradeoff
  • Binary, Multiclass and Multi Label classification
  • Neural Networks 

Also, the candidate should have knowledge of the different metrics used to evaluate the performance of a model. 

Business /Domain Knowledge

The candidate should have a basic understanding of business or the industry in which he is applying as a data scientist. The candidate should be able to understand the problem from the perspective of the company’s business,translate that problem into a data science problem and solve it using the above described skill set. Finally, he should be able to present the insights from the solution effectively although, the depth of the business or domain knowledge will depend upon the experience of the candidate.

 

Using developer assessment software for hiring data scientists

HackerEarth’s developer assessment software, helps to set yourself apart from competing employers and find better talent for your machine learning needs. Customers who have used this software for their machine learning (ML) needs claim that the entire recruitment cycle can be decreased by almost 33% while accelerating the pace at which data science positions are closed. 

HackerEarth’s assessments can help you streamline your data science recruitment in two simple steps:

1.Testing data science skills within a shorter time frame using Data Science questions

Since solving a real-world machine learning problem involves many tasks such as data exploration, data analysis, data pre-processing, model creation, model training and testing, etc., evaluating the skills of the candidates on real-world problems can take a long time. Thus, to assess these skills of the candidates, the platform offers a set of approximate questions where large data sets are broken down into simpler ones so that the candidates can exhibit their skills within the stipulated time frame. This also helps hiring managers to shortlist candidates to work on more in-depth projects or even finalize candidates for entry-level positions.

How to hire a Data Scientist

2. Testing data science skills using elaborate data sets

The developer assessment software also offers the recruiters the opportunity to assess the candidates’ skills on real-world machine learning problems. These questions typically take longer to solve and help to better evaluate candidates before advancing them to interview rounds or rolling out the final offer.

How to hire a Data Scientist

The candidates are given training and testing datasets. The candidates train their model based on the given training dataset and then use that model to predict values of the testing dataset. The candidates finally upload a .csv file (containing the predictions of the testing dataset) along with code file. The platform automatically assesses the predictions submitted by the candidates and generates an accuracy score. The platform provides an option for a leaderboard which sorts the candidates based on the score they receive.

 The platform also allows the recruiter to get an overview of the test and even monitor the performance of all the participants and the currently active participants along with an option to shortlist candidates. Apart from that, the recruiter can request a report of all the participating candidates which is directly mailed to the recruiter’s email. 

Hiring for data science positions is easier with platforms such as HackerEarth’s developer assessment software.

In case you are new to the tool or process, we have compiled a short guide to make it easier

The following steps will ensure that all your check boxes are ticked before making a hire:

1. Know what your organization wishes to achieve with data science

It is imperative for your organization to set the right expectations for the data science platform and your hiring needs to align with it. You could have a bunch of data and very little idea on what to do with it.

In most cases, organizations look at achieving the following using data science:

  • Solve optimization problems: Simply put, reshaping processes by analyzing data; an example could be a logistics company where the supply chain can be optimized so that delivery drivers can use less fuel and reach customers faster
  • Provide recommendations: Using data to form predictive models for companies to better understand their target customers; e-commerce companies use this to recommend products based on buying behavior and also monitor stock levels in warehouses
  • Provide business intelligence: Business Intelligence is all about data management — arranging data and producing information from data via dashboards. These business insights play an important role in the decision-making process of any organization.
  • Some combination of the above: Some organizations also look at combining various aspects from the areas discussed above to derive meaningful insights and also drive product decisions.

2. Define the job parameters for a data scientist

Now that the ultimate goal of data science within your organization has been set, every hiring manager needs to look at certain skills that are important for data scientists to have.

  • Statistics and linear algebra: This is a decision-making skill. Prospective candidates should be good at collecting, analyzing, and making inferences from data.
  • Machine learning: This is the art of classifying or grouping data for prediction. An ideal data scientist should be able to use big data technologies to create pipelines that feed machine learning algorithms.
  • Data mining: This refers to handling and cleaning data. A data scientist should be able to visualize and mine raw data to derive meaningful insights from it.
  • Optimization: A data scientist should be able to maximize the outcome based on factors that he/she can control.
  • Technical skills: Every data scientist should be well versed in the following:

 – Programming languages such as R, Python, Scala, JavaScript, SQL, Spark, C,C++

 – Libraries such as Pandas, Numpy, Scikit-learn, Opencv, matplotlib

 – Data structures and Algorithms, Excel, Tableau, Hadoop, SAS, etc.

Other skills that are good to have for a data scientist include natural language processing, image recognition, time series analysis, econometrics, etc.

3. Know how to assess different types of data scientists

Let us now look at whom to hire. Data scientists are broadly classified into two: Researchers and Engineers. For any organization, it is good to have a mix of both. 

Things to look out for when hiring a researcher

Data researchers have a strong background in math or statistics. They should be skilled to develop custom algorithms to make the most of data and inquisitive to find solutions from data. They should be well-versed in technical skills such as R, Python and SQL. To pull data, candidates should be able to understand Relational Databases. Using SQL to query data is a needed skill and having an experience of storing data using NoSQL is a plus point.

Things to look out for when hiring an engineer

Data engineers typically have a stronger coding background. They should be capable of structuring things well and prototyping quickly. They should be well-versed in data tools and languages such as Python, Scala, Java, and MATLAB. For the extracted data to be used, engineers should be capable of creating a visualization or building a machine learning model. 

4.Understand the data science project lifecycle

Data Science project lifecycle

The most crucial steps for any data science project is the “problem specification” phase where you need to figure out what needs to be solved and the “experimentation and validation” phase where you check whether an approach really works.

Evaluating a candidate’s skills for these important phases can be a tedious process without the right platform to support evaluation. In fact, in a traditional hiring process, most hiring managers feel fortunate if their accuracy of evaluation is as high as 50%. The ongoing effort that traditional hiring requires could easily consume 20% or more of a data science team’s time. This is where a developer assessment platform like HackerEarth ’s comes to the rescue. 

Hiring is one of the most important decisions that you might make as a manager. Use HackerEarth today and experience a world of difference in the way you hire!

 

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About the Author

Soumya Chittigala
Soumya drives product marketing at HackerEarth. When she is not marketing SaaS products,chances are that she's baking cupcakes, binge-watching shows on Netflix or fantasizing about her next travel adventure.

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