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.