ML Engineer to Build Product Recommendation Model

Skills Required

Description

A product recommendation model will be central to improving personalization and boosting user engagement.

The engineer will work with Python, Pandas, and Scikit-learn to process and analyze user interaction data.

Data cleaning and preparation will be the first step, ensuring accuracy and consistency.

Feature engineering will help extract meaningful patterns from raw data.

The recommendation system should adapt to both new and returning users.

Models must be evaluated for accuracy, precision, and recall.

A/B testing will be used to measure the real-world impact of recommendations.

Clear documentation of models and processes is expected.

Scalability is important, as the system will serve a growing user base.

Airflow will be leveraged for automated workflows and pipeline scheduling.

Recommendations must update dynamically as user behavior changes.

The engineer will need to experiment with collaborative and content-based filtering.

Hybrid approaches may also be considered for stronger results.

Performance monitoring tools should be implemented from the start.

Security and privacy of user data must always be respected.

The final system should drive higher conversions and retention.

The outcome will be a recommendation engine that strengthens personalization and supports long-term business growth.