We have a large, messy dataset that needs to be cleaned and prepared for analysis. We're looking for a Python developer for a three-week project to write scripts that will clean, transform, and validate this data. You will use libraries like Pandas to handle missing values, correct inconsistencies, and standardize formats. This is a focused, data-intensive project perfect for a developer who enjoys the challenge of bringing order to chaos.

This is a classic data wrangling project. We have collected a significant amount of data, but it's not yet in a usable state. Your mission, should you choose to accept it, is to turn this raw data into a clean, structured dataset that our analysts can work with.

Your Tasks Will Include:

• Load the raw data (from CSV files) into a Pandas DataFrame.
• Perform an initial exploratory analysis to understand the data's structure and identify common issues.
• Write Python code to handle missing data (e.g., by filling, dropping, or imputing values).
• Correct data entry errors and inconsistencies.
• Standardize data formats (e.g., dates, categorical variables).
• Create new features from existing columns if necessary.
• Validate the cleaned data to ensure its quality.
• Export the final, clean dataset to a new CSV file.

Who is a good fit?

We are looking for a developer who is proficient in Python and has hands-on experience with the Pandas library. You should have a logical, methodical approach to problem-solving and a good eye for detail. This is a great opportunity to apply your data manipulation skills to a real-world problem and deliver a tangible, valuable asset to our company.

• Strong proficiency in Python.
• Hands-on experience with the Pandas library for data manipulation.
• An analytical mindset and attention to detail.
• Good problem-solving skills.
• Experience with data cleaning is a strong plus.

• A focused, data-centric project with a clear goal.
• Deepen your expertise with the Pandas library.
• Deliver a high-value asset to our data science team.
• Fully remote with a flexible schedule.