Applied Scientist - E-Commerce Recommendations / Personalization

Job Description Job Description This is a 100% Remote position and is Salaried + Equity. Our client is a Series A Startup leveraging generative AI to build game-changing B2C technologies that will revolutionize the online shopping experience for consumers. We are seeking a Principal Applied Scientist to spearhead our recommendation and personalization systems. This role will drive the research, development and implementation of our recommendation algorithms. The role extends beyond traditional boundaries, requiring the candidate to innovate in model optimization, system scalability, and the integration of cutting-edge AI technologies to redefine conversational experiences. As a principal scientist & engineer, you will set the technical direction and mentor a team of ML engineers, influence our AI strategy, and collaborate with cross-functional teams to drive product innovation and excellence. Key Responsibilities: Architect and oversee the development of a recommender system Develop a product ranking model that takes into account both customer profiles and dialog context Find the right balance between exploration and optimization to curate recommendations that improve conversion rates in the long run. Working with Product and Engineering counterparts, translate customer needs and business requirements into long term vision and roadmap for recommendation and personalization Act as a thought leader within the company, staying abreast of the latest advancements in the Generative AI field and integrating innovative technologies into our systems to maintain a competitive edge. You Ph.D.in Computer Science, Mathematics, or another quantitative field or outstanding professional experience in machine learning engineering. Over 5 years of industry experience in machine learning, with a proven track record of developing ML-driven products at scale. Experience building recommender systems, preferably in the e-commerce space. Demonstrated expertise in tackling complex problems using machine learning frameworks like PyTorch. A comprehensive understanding of ML system design in production environments, including a quantitative approach to evaluating performance bottlenecks and cost/performance trade-offs. Hands-on experience with large-scale data systems, sophisticated data models, and batch and streaming data pipelines. Quick adaptability to our technology stack encompasses GCP, Kubernetes, Airflow, Pandas, PyTorch, Python, and Node.js. Deep experience in model deployment strategies, utilizing both in-application static methods and dynamic approaches with frameworks like Flask, FastAPI, or equivalents. #ZR #J-18808-Ljbffr