Job Summary
The Data Scientist ML Engineer will design, develop, optimize, and scale machine learning models for recommendation and personalization systems within retail advertising and consumer marketing environments. The role will focus on translating consumer behavior and marketing data into personalized experiences that improve customer engagement, conversion, and revenue. The engineer will work with large-scale consumer datasets, integrate models into production and campaign pipelines, evaluate recommendation quality, and explore advanced machine learning techniques to improve personalization and marketing outcomes.
Key Responsibilities
• Design and develop machine learning models for recommendation and personalization systems, including collaborative filtering, deep learning, and hybrid approaches.
• Develop recommendation solutions for consumer marketing use cases such as product recommendations, next-best-action, and audience targeting.
• Optimize models for scalability, performance, and real-time predictions across large-scale consumer datasets.
• Collaborate with business leaders, marketing partners, product teams, and engineering teams to integrate models into production and campaign pipelines.
• Analyze and improve recommendation quality using metrics such as precision, recall, click-through rate, conversion, and customer lifetime value.
• Leverage customer segmentation, behavioral data, and first-party marketing data to improve personalization and relevance.
• Experiment with advanced techniques such as reinforcement learning, graph neural networks, and contextual bandits to enhance recommendations and marketing outcomes.
Required Qualifications
• 5+ years of experience in machine learning, with a focus on recommendation systems.
• Retail advertising and recommendation systems experience.
• Experience in consumer marketing, retail, e-commerce, or a related consumer-facing domain.
• Proven experience building personalization or recommendation models that improved engagement or marketing performance.
• Proficiency in Python and machine learning frameworks such as TensorFlow or PyTorch.
• Strong understanding of algorithms including matrix factorization, neural networks, and ranking systems.
• Strong understanding of LLMs and agentic AI frameworks that can be customized for recommender systems.
• Experience working with consumer and marketing data, including behavioral, transactional, and campaign data.
• Experience with Databricks and AWS.
• Strong problem-solving skills and ability to develop customer-focused machine learning solutions.
Preferred Qualifications
• Familiarity with Customer Data Platforms (CDPs).
• Experience with marketing analytics.
• Experience with A/B testing.
• Experience with advanced recommendation techniques such as reinforcement learning, graph neural networks, or contextual bandits.
• Non-local candidates willing to relocate may be considered.
