Developing and implementing personalized recommendation systems that enhance user experience is a key responsibility of this role.
Key responsibilities include designing, building, and deploying scalable recommendation engines using various approaches such as collaborative filtering, content-based methods, or hybrid approaches.
* Analyzing macro and micro fashion trends to influence product rankings.
* Extracting insights from large-scale user data and converting them into actionable models.
* Working closely with engineers and product managers to integrate models into production.
* Developing and monitoring metrics for model performance and user engagement impact.
Required skills and qualifications include:
* 2+ years of experience in data science, ideally in e-commerce or consumer-tech.
* Hands-on experience building and deploying recommendation systems (e.g., matrix factorization, deep learning-based recommenders, implicit/explicit feedback models).
* Proficiency in Python and machine learning libraries (e.g., Scikit-learn, TensorFlow, PyTorch, LightFM).
* Experience with data analysis tools such as SQL, Pandas, and Jupyter.
* A strong grasp of personalization techniques and user segmentation strategies.
* Solid understanding of product ranking using behavioral data and trend signals.
* Experience working with large-scale data pipelines and A/B testing frameworks.
* Strong communication and problem-solving skills.
Preferred qualifications include:
* Experience in the fashion or lifestyle e-commerce domain.
* Knowledge of modern MLops workflows and model monitoring tools.
* Familiarity with cloud platforms (AWS, GCP) and tools like Airflow or DBT.