How I Built an AI Agent in Python That Thinks Like a Data Scientist

It started with a challenge I kept facing at work: repetitive data analysis. Every Monday morning, I’d spend hours downloading reports, cleaning up CSV files, running models, and summarizing the results for our product team. It wasn’t difficult work, but it was time-consuming. So I asked myself: Why can’t a Python script do all this? That one question led me down a rabbit hole that changed how I approach data science completely.

What I ended up building wasn’t just a script. It was a lightweight AI agent, an autonomous tool that could collect data, analyze it, generate insights, and even suggest decisions. In other words, it started thinking like a data scientist.

Define the Agent’s Role

Before writing any code, I sketched out what I wanted the agent to do. It needed to:

• Pull fresh data from our internal APIs and public sources.

• Clean and preprocess the data intelligently.

• Run exploratory data analysis (EDA).
• Apply a machine learning model for predictions.

• Summarize key findings in plain English.

• Recommend actions based on the results.

I didn’t want this to be a chatbot or dashboard. I wanted something that felt proactive, like a virtual junior analyst.

Collecting the Right Data

I used requests and pandas to grab CSV exports from Google Sheets, and fetch JSON data from our APIs. The agent was smart enough to timestamp each file and store it in an organized folder structure using os and datetime.

For public data (like industry benchmarks), I used BeautifulSoup for web scraping. This way, the agent could always compare our internal numbers with the broader market.

Data Cleaning and Feature Engineering

This was the part I usually spent most time on, so I made the agent intelligent here.

Using pandas, missingno, and featuretools, it could:

• Fill missing values using logic (mean, median, forward fill).

• Encode categorical variables automatically.

• Detect outliers and handle them gracefully.

• Generate meaningful new features (ratios, trends, group-by aggregates).

I built modular functions that could be reused across different datasets. That alone saved me hours each week.

Building the Machine Learning Core

For predictive tasks (like forecasting churn or revenue), I used scikit-learn, xgboost, and CatBoost. The agent would automatically split the data into training/testing sets, tune hyperparameters using GridSearchCV, and evaluate results using metrics like accuracy, AUC, and F1-score.

But the real magic was in the decision layer.

Instead of just showing model outputs, the agent would interpret results using SHAP values and provide insights like:

“Customer segment A shows a 42% higher churn rate. The top contributing factors are session frequency and account age.”

That kind of context made it feel more like a colleague than a tool.

Talking Like a Data Scientist

Here’s where I added a simple integration with OpenAI’s API. After the model finished running, the agent would pass the results to a language model to generate a narrative summary. Something like:

In the last week, new sign-ups have declined by 15%. The decline is most notable in the 18–25 age group, possibly due to decreased ad spend in that segment.”

It wasn’t perfect, but it added a human touch to the output. Our product managers started reading the summaries and using them in meetings.

Deploying the Agent

I wrapped the whole system into a Python package and created a command-line interface using argparse. Then I deployed it on an AWS EC2 instance and scheduled it with APScheduler to run every Monday at 7 AM.

The agent would email the results to our team's Slack channel using smtplib and the Slack API.

Final Thoughts

This project changed how I see automation in data science. It wasn’t just about saving time, it was about scaling myself. The agent doesn’t replace me, but it gives me superpowers. I can focus on strategy, experimentation, and deeper insights while the agent handles the routine grind.

If you're a data scientist juggling repetitive tasks, I highly recommend building your agent. Python gives you everything you need. And once you see your code start making decisions for you, it’s hard to go back.