Senior Data Scientist Skill Test

Skill summary

The senior data scientist skill test evaluates project leaders who drive data-driven insights with business stakeholders to create a roadmap for revenue growth. They are involved in the application of machine learning, statistical methods, and exploratory analysis for extracting insights from data.

As a crucial role, senior data scientists are expected to have strong communication skills to effectively convey their findings to both technical and non-technical stakeholders.


Multiple Choice Questions


10 min






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Why we created this test

The senior data scientist test evaluates the expertise in technical, communication, and project management skills to lead data-driven projects and teams effectively such as statistical analysis, business acumen, and problem-solving skills.

Senior executives who fair well on the assessment have commendable expertise in machine learning algorithms, data visualization,  data storage and retrieval techniques, data pipelines, and data architecture.

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Glider AI skill intelligence platform Senior Data Scientist
Senior Data Scientist Glider AI skill intelligence platform

Skills evaluated

Senior data scientists need to have certain competencies to do their job well. The skills that they should have are:
Pick the right kind of statistical models like logistic regression or xgboost
Experience in using the right kind of metrics like accuracy or F1-score
Standardize data through data ingestion, EDA, API endpoints
Optimize data-based parameters through random search or Bayesian optimization algorithms
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Related roles

Use the senior data scientist skill test to hire for these roles:
Big Data Engineer
Data Architect
Data Security Analyst
Big Intelligence Analyst
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Senior Data Scientist Glider AI skill intelligence platform

Science-backed questions for hundreds of roles

Use these sample questions to evaluate skill and fit for the senior data scientist role before hiring.

1. ______ is the most important language in Data Science
  • Ruby 
  • Python 
  • R 
  • Java 
  • None of these 
2. ______ is not a tool for Statistical Data Analysis
  • Logistic Regression 
  • ANOVA 
  • Histogram 
  • Linear & Non-linear Regression 
  • None of these 
3. ______ is the graphical representation of a data set
  • Visualization 
  • Investigative cycle 
  • Data set 
  • Interpretation 
  • None of these 
4. ______ is outside the data trend
  • Outlier 
  • Trend 
  • Spike 
  • Both 1 & 2 
  • None of these 
5. ______ is not a machine-learning algorithm
  • SVG 
  • SVM 
  • Random Forest 
  • Both 1 & 2 
  • None of these 

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Christina HiltonGlider AI skill intelligence platform

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North America Sales
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