Job Summary
Client is seeking a Machine Learning Engineer with deep expertise in building and optimizing recommendation systems within the consumer marketing space. The ideal candidate will have hands-on experience designing, implementing, and scaling personalized recommendation and targeting models that drive customer engagement, conversion, and revenue growth. Experience translating consumer behavior and marketing data into actionable, personalized experiences is essential. This is a hybrid role requiring onsite presence three days per week in Parsippany, New Jersey, with a final-round onsite interview.
Key Responsibilities
• Design and develop machine learning models for recommendation and personalization systems, including collaborative filtering, deep learning, and hybrid approaches, 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 enhance personalization and relevance.
• Experiment with advanced techniques such as reinforcement learning, graph neural networks, and contextual bandits to improve recommendations and marketing outcomes.
Required Qualifications
• 5+ years of experience in machine learning, with a focus on recommendation systems, ideally within consumer marketing, retail, e-commerce, or a related consumer-facing domain.
• Proven experience building personalization or recommendation models that measurably improve engagement or marketing performance.
• Proficiency in Python and machine learning frameworks such as TensorFlow or PyTorch.
• Strong understanding of recommendation and ranking systems.
• Strong understanding of neural networks and deep learning.
• Strong understanding of graph-based approaches and Graph Neural Networks.
• Strong understanding of DeepFM and matrix factorization techniques.
• Strong understanding of LTMs 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.
• Excellent problem-solving skills and a passion for delivering impactful, customer-centric solutions.
