Data and AI Career Test

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Most people picture data and AI work as someone staring at screens full of numbers, but the reality covers a much wider range of roles than that. You might spend a day building a system that can predict which patients are at risk of a serious illness, another designing the visual dashboards that help a business understand how it is performing, and another working on systems that move and store large amounts of data reliably.

From investigating patterns in data to building systems that can learn and improve on their own, this field is transforming almost every industry in the world. A few minutes could show you which part of it you are most drawn to.

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Disclaimer: Before you start the test, please consider the following: the test results are provided to you for the purpose of discovering your interests, your likes and dislikes and contemplating on what you may want to do in the future. Our tests are not psychological tests, nor do they indicate that you excel in a certain field of interest. Our tests do not amount to professional career advice. Our terms of use contain a disclaimer.

1
Plan AI features for apps and digital products.
2
Examine data to explain what trends mean.
3
Publish research about new AI techniques.
4
Pull together company data into clear summary reports.
5
Develop new methods that make AI systems more accurate.
6
Automate reports so teams get data when they need it.
7
Dig into data to find out why a trend is happening.
8
Create pipelines that move and store large data.
9
Optimize data storage for fast and reliable retrieval.
10
Train models to classify and label data accurately.
11
Produce reports that track business performance.
12
Study how machines learn from data.
13
Test how AI improves product experiences.
14
Build models that predict outcomes based on past data.
15
Spot and fix data errors before they cause problems.
16
Monitor and improve the accuracy of live AI models.
17
Test ideas using experiments and datasets.
18
Interpret model outputs to draw meaningful conclusions.
19
Design systems that collect data from many sources.
20
Build interactive dashboards people can explore.
21
Make visual tools that let users explore data freely.
22
Design visual dashboards that explain complex data.
23
Pick the right visual format for different data types.
24
Write standards for how data should be stored and used.
25
Work with engineers to bring AI features to life.
26
Present findings from data to decision makers.
27
Summarize what a dataset reveals in plain language.
28
Search databases to retrieve business data.
29
Maintain databases that store large volumes of data.
30
Connect data sources to machine learning systems.
31
Create statistical models to test an idea.
32
Build systems that run machine learning models.
33
Set rules that ensure data is accurate and trustworthy.
34
Check that data meets quality rules across a business.
35
Define the requirements for an AI feature with a team.
36
Design experiments to evaluate new AI approaches.
Please answer all highlighted questions.

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