Lower Risk

general

Data Analyst

AI is poised to significantly impact data analysts by automating routine data cleaning, report generation, and basic statistical analysis. LLMs can assist in data summarization and insight generation, while specialized AI tools can handle predictive modeling and anomaly detection. However, tasks requiring critical thinking, complex problem-solving, and communication of insights to stakeholders will remain crucial for human data analysts.

Top risks:

Data Cleaning and Preprocessing75%
Statistical Analysis and Modeling60%
Data Visualization and Reporting80%

Technology

Data Scientist

AI is increasingly impacting data scientists by automating tasks such as data cleaning, feature engineering, and model selection. LLMs are assisting in code generation and documentation, while AutoML platforms streamline model development. However, tasks requiring deep analytical thinking, strategic problem-solving, and communication of complex findings remain largely human-driven.

Top risks:

Data Cleaning and Preprocessing75%
Feature Engineering60%
Model Selection and Hyperparameter Tuning80%

Side-by-Side Metrics

Metric	Data Analyst	Data Scientist
Risk Score	69%	71%
Risk Level	High Risk	Critical Risk
Timeline	2-5 years	2-5 years
Category	general	Technology
Tasks at Risk	7 tasks	8 tasks
Skills at Risk	4 skills	5 skills
Safe Skills	5 skills	5 skills

Risk Comparison

Data Analyst69%

Data Scientist71%

Data Analyst has 2 percentage points lower risk than Data Scientist.

Tasks at Risk

Shared Vulnerable Tasks(2 tasks both jobs share)

Data Cleaning and PreprocessingDeveloping and Maintaining Data Infrastructure

Data Analyst Tasks at Risk

Data Cleaning and Preprocessing75%

1-3 years

Statistical Analysis and Modeling60%

2-5 years

Data Visualization and Reporting80%

1-3 years

Data Mining and Pattern Identification70%

2-5 years

Communicating Insights and Recommendations30%

5-10 years

Data Scientist Tasks at Risk

Data Cleaning and Preprocessing75%

1-3 years

Feature Engineering60%

1-3 years

Model Selection and Hyperparameter Tuning80%

Already possible

Developing and Implementing Machine Learning Models65%

2-5 years

Analyzing and Interpreting Results40%

5-10 years

Skills Comparison

Shared AI-Resistant Skills(valuable in both careers)

Complex problem-solvingCritical thinking

Data Analyst -- Safe Skills

Complex problem-solvingCritical thinkingCommunication of insightsStakeholder managementBusiness acumen

Data Analyst -- Skills at Risk

Data cleaningBasic statistical analysisReport generationData visualization

Data Scientist -- Safe Skills

Complex problem-solvingCritical thinkingCommunication of complex findingsStrategic thinkingEthical considerations in AI

Data Scientist -- Skills at Risk

Data cleaningFeature engineeringModel selectionBasic statistical analysisReport generation

Shared Vulnerable Skills(at risk in both careers)

Data cleaningBasic statistical analysisReport generation

Recommendations

Data Analyst

1Focus on developing strong communication and critical thinking skills. Learn how to translate data insights into actionable business recommendations. Stay updated on the latest AI tools and techniques, but focus on how to leverage them to enhance your analytical capabilities, not replace them. Consider specializing in a specific industry or domain to increase your value.

Data Scientist

1Focus on developing strong analytical and problem-solving skills.
2Learn how to effectively leverage AI tools to enhance productivity.
3Develop strong communication and presentation skills.
4Stay up-to-date with the latest AI research and technologies.
5Consider specializing in areas where human expertise is still critical, such as explainable AI or ethical AI.

View Data Analyst Full Analysis View Data Scientist Full Analysis

Compare Other Jobs

Curious about another career? Search and compare any two jobs.

Weekly Displacement Digest

Get the latest AI job displacement insights, risk score updates, and career recommendations delivered to your inbox every week.

Metric

Data Analyst

Data Scientist

Risk Score

69%

71%

Risk Level

High Risk

Critical Risk

Timeline

2-5 years

Shared Vulnerable Tasks(2 tasks both jobs share)

Data Cleaning and PreprocessingDeveloping and Maintaining Data Infrastructure

Data Analyst Tasks at Risk

Data Cleaning and Preprocessing75%

1-3 years

Statistical Analysis and Modeling60%

2-5 years

Data Visualization and Reporting80%

1-3 years

Data Mining and Pattern Identification70%

2-5 years

Communicating Insights and Recommendations30%

5-10 years

Data Scientist Tasks at Risk

Data Cleaning and Preprocessing75%

1-3 years

Feature Engineering60%

1-3 years

Model Selection and Hyperparameter Tuning80%

Already possible

Developing and Implementing Machine Learning Models65%

2-5 years

Analyzing and Interpreting Results40%

5-10 years

Skills Comparison

Shared AI-Resistant Skills(valuable in both careers)

Complex problem-solvingCritical thinking

Data Analyst -- Safe Skills

Complex problem-solvingCritical thinkingCommunication of insightsStakeholder managementBusiness acumen

Data Analyst -- Skills at Risk

Data cleaningBasic statistical analysisReport generationData visualization

Data Scientist -- Safe Skills

Complex problem-solvingCritical thinkingCommunication of complex findingsStrategic thinkingEthical considerations in AI

Data Scientist -- Skills at Risk

Data cleaningFeature engineeringModel selectionBasic statistical analysisReport generation

Shared Vulnerable Skills(at risk in both careers)

Data cleaningBasic statistical analysisReport generation

Recommendations

Data Analyst

1Focus on developing strong communication and critical thinking skills. Learn how to translate data insights into actionable business recommendations. Stay updated on the latest AI tools and techniques, but focus on how to leverage them to enhance your analytical capabilities, not replace them. Consider specializing in a specific industry or domain to increase your value.

Data Scientist

1Focus on developing strong analytical and problem-solving skills.
2Learn how to effectively leverage AI tools to enhance productivity.
3Develop strong communication and presentation skills.
4Stay up-to-date with the latest AI research and technologies.
5Consider specializing in areas where human expertise is still critical, such as explainable AI or ethical AI.