

Picture by the writer
. Introduction
As a data scientists, we wear a lot of hats on the job that often looks like multiple careers roam one. In the same day of work, I have to do:
- Create data pipelines with
SQL
AndPython
- Use data to analyze data
- Talk to Stakeholders Recommendations
- Permanently monitor product performance and prepare reports
- Run experiences to help the company decide whether to launch a product or not
And this is half of it.
It is interesting to be a data scientist because it is one of the most versatile fields in Tech: you find many different aspects of the business and can imagine the effects of products on everyday users.
But the negative side? It feels like you are always playing a catchup.
If a product launch performs poorly, you will need to know why – and you should do so immediately. In the meantime, if a stakeholder wants to understand the impact of launching feature A instead of Stakeholder Feature B, you need to design an experience faster and have to explain the results that are easy to understand.
You may not be too technical in your explanation, but you may not be too ambiguous. You have to find a middle ground that balances the interpretation with analytics strictly.
By the end of the work day, It sometimes feels like i just run a marathon. Just get up and do it again the next day. So when I have the opportunity to automatically make parts of my job with AI, I take it.
Recently, I have begun to add AI agents to my data science workflows.
It has made me more efficient in my job, and I can answer with data in response to business questions that I am faster than ever.
In this article, I would exactly explain how I use AI agents to automatically make parts of my data science work flu. Specifically, we will discover:
- How do I usually perform data science workflow without AI
- Steps taken to automatically make the work flu with AI
- I use exactly tools and how much time it has saved me
But before we enter it, let’s review what the AI agent is and why there is so much hype around them.
. What are AI agents?
AI agents are a large language model (LLM) power system that can automatically work through a problem by planning and arguing. They can be used to automate modern workflows without the clear direction of the user.
It can look like running a single command and processing the workflow from the end of the LLM to finally and in the whole process, from the end to end the workflow. You can use this time to focus on other tasks without the need to interfere with or monitor each step.
. How do I use AI agents to automatically make experiments in Data Science
Experience data is a huge part of the science job.
Companies like Spatifs, Google, and MetaThis always experience before issuing a new product before understanding:
- Whether new products will provide more profits on investment and is capable of allocated resources for its construction
- If a long -term positive impact on the product platform will have
- User feelings around this product launch
Data scientists usually do A/B tests to determine the effectiveness of a new feature or product. More information about A/B testing in data science, you can read this guide on A/B testing.
Companies can have 100 experiments a week. Experience design and analysis can be a very frequent process, which is why I decided to try to automate it using AI agents.
Here I usually analyze the results of an experience, a process that takes three days a week:
- Create SQL pipelines to extract A/B test data that flows from the system
- Inquire from these pipelines and perform research data analisis (EDA) to determine the type of statistical test type to use
- Write azigar code to run the data test and imagine this data
- Prepare a recommendation (for example, reach this feature to our 100 % users)
- Submit this data in the form of Excel sheet, document, or slide deck and describe the results to the stakeholders
2 and 3 steps are the most time -consuming because the results of the experience are not always straightforward.
For example, when you decide whether to roll the video advertisement or image ad, we can get contradictory results. A photo advertising can produce more quick purchases, causing short -term income. However, video ads can better maintain and loyal the user, which means that users make more purchases. This increases long -term income.
In this case, we need to submit more auxiliary data points to decide whether to launch a photo or video ads. We have to use different data techniques and perform some imitation to find out which point of view is best with our business goals.
When this process becomes automatic with an AI agent, it removes a lot of manual interference. We can collect data to AI and perform this deep dive for us, which relieves analytical heavy lifting that we usually do.
Here seems to be like Ai/B test analysis with AI agent:
- I use CursorAn AI editor who can access your code base and automatically write and edit your code.
- The model using the protocol (MCP) of the context, accesses the cursor data leak where raw experience data flows
- The cursor then automatically forms a pipeline for action on experimental data, and re -accessed the data leak to join it with other related data tables.
- After creating all the necessary pipelines, it performs the EDA on these tables and automatically determines the technique of data techniques to use to analyze the A/B test results.
- It runs the selected statistical test and analyzes the output, automatically creates a comprehensive HTML report of the output that is presented to business stakeholders.
From the end of an AI agent mentioned above, the experience is the automation framework at the end.
Of course, once this process is completed, I look at the results of the analysis and go through the steps taken by the AI agent. I have to admit that this workflow is not always smooth. AI deceives and requires examples of a ton of indicators and advance reviews before it comes with its own workflow. The “garbage out of garbage out” is definitely applied here, and I just did a week to support examples and build quick files to ensure that the Corcer has all the relevant information needed to run this analysis.
There were many ahead and many repetitions before the expected automatic framework.
Now that this AI agent works, however, I am able to dramatically reduce the amount of time spent to analyze the A/B test results. While the AI agent performs this workflow, I can focus on other tasks.
It takes work from my plate, which makes me a little busy data scientist. I also have to produce results in front of stakeholders, and short -time helps the entire product team make sharp decisions.
. Why should you learn AI agents for data science
I know every data professional has added AI to its workflow. Organizations have a top download for making rapid business decisions, launching products faster and staying beyond competition. I am sure that it is very important for data scientists to be relevant to the AI in order to stay relevant and stay competitive in this job market.
And in my experience, we need advanced to make agent workflows to automatically make parts of our jobs automatically. I had to learn new tools and techniques such as indicating on MCP Configure, AI Agent (which is different from typing immediately Chat GPT), And workflow orchestration. The initial learning curve is worth it because once you are able to automate parts of your job, it saves hours.
If you are a data scientist or want, I advise you to learn how to build AI-Assisted workflow at the beginning of my career. This is becoming increasingly rapidly for the industry’s expectation rather than just a good to work, and you should start keeping yourself in the future of the data role.
To start, you can Watch this video A step -by -step guide to learning agent AI for free.
Natasa Selorj Is a self -educated data scientist with a passion for writing. Natasa writes on everything related to science, which is a real master of all data titles. You can contact with it Linked Or check it out Utube channel.