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

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