Principal Full-Stack Data Scientist - Foundational Models

Accountabilities:: Design, develop, evaluate, and deploy production-ready machine learning models that improve personalization, recommendations, and search-related experiences. Advance foundational modeling capabilities, including client and item representations, embeddings, retrieval and ranking systems, recommendation algorithms, and assortment generation. Explore and implement modern machine learning approaches, including large language models (LLMs), deep learning, representation learning, multimodal modeling, and generative AI. Own the end-to-end machine learning lifecycle, from problem definition, data exploration, and prototyping to experimentation, production deployment, monitoring, and continuous improvement. Develop rigorous offline evaluations and online experiments, including A/B tests, to assess model performance and quantify client and business impact. Analyze and process large-scale behavioral and product datasets using Python, SQL, and distributed computing technologies. Build reliable, scalable, and production-quality ML systems while balancing model quality, latency, computational cost, observability, and operational resilience. Collaborate with Product, Engineering, Data Science, and other technical teams to understand downstream requirements and deliver reusable foundational models and capabilities. Contribute to the team's technical direction through architecture and design discussions, research, prototyping, code reviews, and the development of modeling and engineering best practices. Communicate complex modeling approaches, technical trade-offs, research findings, and recommendations clearly to technical and non-technical stakeholders. Mentor fellow Data Scientists and engineers, share expertise, and help elevate technical standards across the organization. Requirements Bachelor's degree in Computer Science, Statistics, Mathematics, Physics, or another quantitative discipline; a Master's degree or PhD is preferred. At least 8 years of experience designing, developing, and deploying machine learning solutions, ideally in personalization, recommendation systems, search, or representation learning. Strong Python programming skills, with the ability to architect technical solutions and develop maintainable, production-grade code. Demonstrated ability to independently lead ambiguous machine learning problems from initial exploration and prototyping through deployment, monitoring, iteration, and measurable business impact. Experience working with large-scale datasets using SQL and distributed data-processing technologies such as Apache Spark. Hands-on experience with modern deep learning frameworks, including PyTorch or TensorFlow. Strong understanding of model evaluation and experimentation, including offline metrics, A/B testing, statistical assessment, and the translation of model improvements into measurable product outcomes. Ability to assess and manage production ML trade-offs involving predictive quality, scalability, latency, reliability, infrastructure requirements, and computational costs. Experience developing or applying modern machine learning techniques, such as deep learning, embeddings, representation learning, recommendation algorithms, LLMs, or multimodal models. Strong technical leadership and problem-solving skills, with the ability to establish architecture, influence technical decisions, and drive projects forward in complex or ambiguous environments. Excellent communication and collaboration skills, including the ability to explain complex technical concepts to diverse audiences and build alignment across cross-functional teams. Intellectual curiosity, adaptability, and a demonstrated commitment to continuous learning and applying emerging research to practical challenges. Benefits Annual salary: $200,000–$237,000 USD, with compensation determined by experience, professional background, location, and other relevant factors. Equity compensation: Eligibility for new-hire and ongoing restricted stock unit (RSU) grants, subject to applicable performance and company conditions. Annual bonus: Eligibility for an annual bonus. Comprehensive healthcare: Medical, dental, and vision insurance. Additional benefits: Access to other employee benefits and health and wellness programs. Remote flexibility: Opportunity to work remotely within the United States. Collaborative culture: Work alongside talented, multidisciplinary professionals in an environment that values innovation, integrity, trust, and diverse perspectives. Professional growth: Opportunities to influence technical strategy, explore emerging AI and machine learning techniques, and contribute to high-impact projects. Ownership and impact: A culture that encourages autonomy, creative problem-solving, continuous learning, and accountability. Values-driven environment: A commitment to inclusion, equity, sustainability, teamwork, and meaningful client experiences.