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Data Scientist Test

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Type

Role Specific Skills

Time

10 minutes

Level

Medium

Questions

10

About the Test

The Data Science Test for hiring data scientists is of intermediate difficulty and assesses advanced concepts like Machine Learning Techniques, Data Visualization, and Statistical Modelling.

This assessment can be used to recruit mid-level data scientists and test their intermediate-level skills to perform data science operations. It evaluates their ability to work with data and their knowledge of machine learning & its techniques, statistical analysis of the data, and neural networks. The candidate’s skill to automate data, build machine learning models, track the models, and monitor & retrain models using TensorFlow can also be analyzed using this test.

The Data Scientist test helps assess individuals with a few years of hands-on experience working with data. It can help narrow down data scientists who have the capacity to create machine-learning-based tools with extensive knowledge of operations such as data extraction and mining, data cleaning, using data classification & regression algorithms, performing data wrangling, conducting advanced analytics, and leveraging deep-level machine learning techniques to deliver valuable business-focused insights.

For hiring to fill job roles like Data Scientists, Data Analysts, and Data Engineers, which require a few years of experience in the field, the Data Science (Intermediate) Assessment can be used. Candidates who score well on this test will possess adequate skills to work closely with the business and use data to achieve the company’s objectives of increasing operational efficiency, identifying new business opportunities, and improving marketing and sales programs.

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Skills Measured

  • Machine Learning Techniques
  • Data Visualization
  • Statistical Modeling
  • Tensorflow
  • Deep Dive into Machine Learning Models

Roles

  • Data Scientists
  • Data Analysts
  • Data engineers
  • Data Science Professionals
  • Data Science Architect
  • Senior Software engineers

Recruiting for Data Science

Assessing experienced data scientists becomes easy with the Data Science (Intermediate) Test. It can accurately measure the candidate’s skills for performing data extraction, cleaning, and analysis using software tools and codes, with objective questions on advanced data science concepts. The test delves into some critical knowledge required in a candidate, such as the following:

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1

Machine Learning Techniques

The various machine learning algorithms a data scientist should be familiar with, viz., data regression, classification, clustering, etc., are evaluated in this test. It poses queries on how to select an accurate ML technique that can be used for the given dataset and how to use the ML technique to achieve the desired functionality in machine learning models.

2

Data Visualization

It is essential for viewing data to understand trends, patterns & outliers better to make informed business decisions. Candidates are tested on their technical skills to use data visualization tools and draw accurate conclusions from data charts in this assessment. 

3

Statistical Modeling

The individual’s ability to build different types of statistical models, viz. parametric, nonparametric, and semiparametric, to gather, organize, analyze, interpret, and statistically, design data are evaluated in this test. 

4

TensorFlow

TensorFlow is an end-to-end machine learning platform used to build, train, and maintain ML models. The test takers are gauged on their skill in working with TensorFlow’s standard datasets, data pipelines, preprocessing layers, and other tools. 

5

Deep Dive into Machine Learning Models

The assessment also evaluates the candidate’s deeper level of knowledge on machine learning models such as supervised, unsupervised, and reinforcement learning and their algorithms like Linear Regression, Logistic Regression, Decision Tree, etc. 

The test is created by a subject-matter expert

Testlify’s skill tests are designed by experienced SMEs (subject matter experts). We evaluate these experts based on specific metrics such as expertise, capability, and their market reputation. Prior to being published, each skill test is peer-reviewed by other experts and then calibrated based on insights derived from a significant number of test-takers who are well-versed in that skill area. Our inherent feedback systems and built-in algorithms enable our SMEs to refine our tests continually.

View sample score cards

Top five hard skills interview questions for Data Scientist

1. Can you explain what a random forest is and how it works, and give an example of when it might be used in a data science project?
Why this Matters?

Random forests are a commonly used machine learning algorithm in data science, and understanding how they work is an important skill for an intermediate data scientist.

What to listen for?

A good candidate should be able to explain what a random forest is and how it works, including the concept of combining many decision trees to make a prediction. They should also be able to provide an example of when a random forest might be used in a data science project, such as for classification or regression problems, and discuss the benefits of using a random forest over a single decision tree.

2. Can you explain what deep learning is and how it differs from traditional machine learning?
Why this Matters?

Deep learning is a subfield of machine learning that has gained significant attention in recent years, and understanding the differences between deep learning and traditional machine learning is an important skill for an intermediate data scientist.

What to listen for?

A good candidate should be able to explain what deep learning is, including the concept of neural networks, and how it differs from traditional machine learning. They should also be able to discuss the types of problems that deep learning is well-suited for and the types of problems that are better suited for traditional machine learning algorithms.

3. Can you explain what regularization is and how it can be used to prevent overfitting in a machine learning model?
Why this Matters?

Regularization is a technique used to prevent overfitting in machine learning models, and understanding how it works is an important skill for an intermediate data scientist.

What to listen for?

A good candidate should be able to explain what regularization is and why it is important, including the concept of adding a penalty term to the loss function to reduce model complexity. They should also be able to discuss the different types of regularization, such as L1 and L2 regularization, and how to implement them in code.

4. Can you explain what feature engineering is and why it is important in data science?
Why this Matters?

Feature engineering is the process of creating new features from existing data, and it is an important skill for an intermediate data scientist as it can have a significant impact on model performance.

What to listen for?

A good candidate should be able to explain what feature engineering is and why it is important, including the concept of creating new features that better capture the underlying relationships in the data. They should also be able to discuss different feature engineering techniques, such as one-hot encoding, scaling, and normalization, and how to implement them in code.

5. Can you explain what a confusion matrix is and how it can be used to evaluate the performance of a classification model?
Why this Matters?

A confusion matrix is a commonly used tool to evaluate the performance of a classification model, and understanding how it works is an important skill for an intermediate data scientist.

What to listen for?

A good candidate should be able to explain what a confusion matrix is and how it can be used to evaluate the performance of a classification model, including the concepts of true positive, false positive, true negative, and false negative. They should also be able to discuss how to interpret the results of a confusion matrix and how to use it to make informed decisions about model performance.

Frequently Asked Questions for Data Scientist

The Data Science (Intermediate) test helps assess individuals with a few years of hands-on experience working with data. It can help narrow down data scientists who have the capacity to create machine-learning-based tools with extensive knowledge of operations such as data extraction and mining, data cleaning, using data classification & regression algorithms, performing data wrangling, conducting advanced analytics, and leveraging deep-level machine learning techniques to deliver valuable business-focused insights.

The Data Science Test for hiring data scientists is of intermediate difficulty and assesses advanced concepts like Machine Learning Techniques, Data Visualization, and Statistical Modelling.

  • Data Scientists
  • Data Analysts
  • Data engineers
  • Data Science Professionals
  • Data Science Architect
  • Senior Software engineers

  • Machine Learning Techniques
  • Data Visualization
  • Statistical Modeling
  • Tensorflow
  • Deep Dive into Machine Learning Models

  • Collecting and cleaning data from a variety of sources, including databases, sensors, and social media platforms.
  • Using statistical techniques and tools to analyze and interpret data.
  • Communicating findings and recommendations to stakeholders using visualizations, reports, and other forms of presentation.
  • Collaborating with cross-functional teams to understand data needs and identify opportunities for data-driven decision-making.

Frequently Asked Questions (FAQs)

Want to know more about Testlify? Here are answers to the most commonly asked questions about our company.

These are ready-made tests, existing in our test library, created by subject matter experts. We have 200+ such skills tests covering various skills from programming skills to DevOps, including Aptitude and Personality testing.

You can go to the ‘Test Library’ tab and search for tests from the Job Role or Test Type drop-down menu.

Currently, we do not offer any sample tests. However, when you select a test, there would be a few practice questions before the start of the actual test, which would give you a fair idea of how the entire test would look.

Our tests typically take between 25-30 minutes to complete.

In case you need to test for a unique skill-set or specialized experience, you can add your own questions and customize the test to suit your requirements.

We’ve put a lot of effort into ensuring a conducive test environment. A stable internet connection, an internet browser with cookies and Java-Script enabled is all that is required!
However, for a smooth test-taking process, we recommend the following browsers:
– Google Chrome
– Internet Expl
– Mozilla Firefox

Yes, our tests are compatible with almost all modern mobile devices (including tablets) that have a webcam installed.

Yes, our tests are EEOC (Equal Employment Opportunity Commission) compliant and are whetted by subject matter experts, thus having high reliability (test consistency) and validity (accuracy of the test).

Yes! We integrate with popular recruiting tools such as Greenhouse, Lever, GoodTime, and many more.

We are GDPR compliant and take data security very seriously. You have complete ownership of your data. All your data is safe and secure, and we do not expose it to any third party.

Testlify is an excellent tool for assessing candidates’ soft and hard skills. The founder and customer success team were helpful during onboarding and demonstrated a commitment to continuously improving the platform.
Fabrizio Parzanese
Founder, ExpHire
Testlify’s integration with ATS makes it simple to incorporate into recruitment processes. Customizable test suites, open-ended questions, and qualifying questions streamline the candidate assessment process and improve collaboration with hiring managers. 
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 Founder, White
A must-have tool in any HR department! Well thought out a comprehensive platform that shortens the candidate selection time significantly.
Meir Shachar
CEO at PowerLinx
With Testlify, we were able to optimize our initial screening process by upwards of 75%.
We saved a tremendous amount
Gary E. Benedik
President, Arch Advisory Group
Testlify has revolutionized our hiring process by streamlining the screening stage. It has saved us countless hours by allowing us to shortlist the right candidates.
Vipin Kashyap
CEO at Sookshum Labs

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