Data Science & Engineering Intern

Company Description

Razonica helps retail and e-commerce companies grow faster. Our product/ services help retail and e-commerce companies find growth opportunities without needing to depend on their ability to pay for experience or resort to trial and error. Both can be expensive.
We are replacing this trial and error with deep analytics and predictive modeling so that the companies can be built faster.

Role Description
This is a full-time remote role for a Data Science & Engineering Intern. The intern will assist in data analysis, data science projects, and the application of statistical methods. The intern will conduct data analytics tasks including data cleaning, data preparation, and exploratory data analysis. Additionally, the intern will work on building, testing, and deploying machine learning models, and generate insights from data to support business decisions.

What You’ll Work On

As a Data Science & Engineering Intern, you'll work on high-impact, real-world projects. Your responsibilities will include:

Building data engineering pipelines to enable efficient data aggregation
Performing descriptive and exploratory data analysis
Collaborating with the team to build and refine ML hypotheses
Scraping, cleaning, and preprocessing diverse data sources
Designing and training ML models (clustering, time series, predictive modeling, etc.)
Translating model results into actionable insights
Documenting your work clearly for feedback and reproducibility

You’ll be assigned 4 projects per month, and you’ll have the opportunity to work independently with regular team check-ins and mentorship.

What We’re Looking For

You're a great fit if you have a curious mind, a hands-on approach, and a strong foundation in:

Python (pandas, Num Py, scikit-learn, etc.)
Data engineering and pipeline design
Data cleaning and preprocessing
ML modeling (supervised and unsupervised techniques)
Data visualization (Matplotlib, Seaborn, Plotly, or similar)

Bonus points if you’ve worked on real-world projects!

Internship Details

Time Commitment: 12–18 hours/week
Duration: 6 months
Compensation:
Project-based payouts depending on performance
Letters of Recommendation from the company or senior data scientists based on performance