Description

Key Responsibilities:

• Design, build, and optimize machine learning recommendation and ranking models for personalized products, ads, promotions, and offers.
• Develop ML models that improve ad targeting, relevance, personalization, conversion, and campaign performance within a retail environment.
• Build recommendation solutions using techniques such as collaborative filtering, learning-to-rank, matrix factorization, deep learning, and hybrid approaches.
• Analyze large-scale customer, transaction, behavioral, and advertising datasets to identify opportunities and improve model performance.
• Design experiments and measure model/business impact using A/B testing and relevant ML and advertising metrics.
• Partner with data engineering, product, advertising, and business teams to deploy and scale models in production.

Requirements:

• 5+ years of experience in Data Science, Machine Learning Engineering, or applied ML.
• Hands-on experience building and deploying recommendation, personalization, or ranking models.
• Experience applying machine learning within retail media, digital advertising, AdTech, e-commerce, or retail personalization.
• Strong Python and experience with TensorFlow, PyTorch, Scikit-learn, or similar ML frameworks.
• Strong understanding of recommendation algorithms, ranking, customer segmentation, and model evaluation.
• Experience with Databricks and AWS strongly preferred.
• Experience taking ML models from experimentation through production deployment and ongoing optimization

Key Skills

Education

Bachelor's degree