I’m working on a data science project with time-ordered data, and I’m seeing a significant drop in model performance once I move from training to validation. I’m sharing a simplified version of the problem and code below.

The dataset represents events over time, and the target is binary. I initially used a random train-test split, but later switched to a time-based split to better reflect real-world usage. After this change, performance dropped sharply, and I’m trying to understand whether this is expected or if I’m doing something wrong.

Here’s a simplified version of the code:

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import roc_auc_score

# sample data
df = pd.read_csv("data.csv")
df = df.sort_values("event_time")

X = df.drop(columns=["target"])
y = df["target"]

# time-based split
split_index = int(len(df) * 0.8)
X_train, X_test = X.iloc[:split_index], X.iloc[split_index:]
y_train, y_test = y.iloc[:split_index], y.iloc[split_index:]

model = RandomForestClassifier(random_state=42)
model.fit(X_train, y_train)

preds = model.predict_proba(X_test)[:, 1]
print("AUC:", roc_auc_score(y_test, preds))

With a random split, the AUC was around 0.82.
With the time-based split, it drops to around 0.61.

I’m trying to understand:

• Is this performance gap a common sign of data leakage in the original setup?

• Are tree-based models like Random Forests particularly sensitive to temporal shifts?

• What are good practices to diagnose whether this is concept drift, feature leakage, or simply a harder prediction problem?

• Would you approach validation differently for time-dependent data like this?

Looking for general guidance, validation strategies, or patterns others have seen in similar scenarios.

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Today, the competitive advantage is less about who can write the best model from scratch and more about who can frame the right problem, define meaningful metrics, interpret model outputs responsibly, design data loops, and understand the business impact of predictions. Even the most complex models LLMs, multimodal architectures, time-series forecasters can now be deployed with pre-built frameworks or API calls.

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Hi everyone,

I’m currently an undergraduate student in Data Science, actively working toward becoming a data scientist. So far, I’ve built a foundation with basic machine learning models using libraries like Pandas, NumPy, Matplotlib, Scikit-learn, and some PyTorch. I’ve also explored LLMs by working with pre-trained models through Hugging Face and LangChain. Lately, I’ve been diving into more advanced ML and deep learning concepts, setting up CI/CD pipelines, and learning backend development for ML using FastAPI and Flask.

Despite experimenting with this wide range of tools and technologies, I still find myself unclear about what companies actually expect from data scientists—both at junior and senior levels. What tech stack should I focus on? Which trends and skills are truly valued in the industry?

As a student, it’s hard to get a clear answer on this. Could someone with experience in the field help clarify what companies are really looking for in data scientists today?

Thanks in advance!