Principal Full-Stack Data Scientist – Foundational Models

This is a fully remote position, open to applicants in United States.

📋 Description

• Design, develop, assess, and implement machine learning models that enhance personalization and recommendation systems.

• Improve foundational modeling capabilities, including client and item representations, embeddings, retrieval, ranking, recommendation models, and assortment generation.

• Investigate and utilize LLMs, deep learning, representation learning, multimodal modeling, and generative techniques.

• Manage the entire ML lifecycle from problem definition and data exploration to modeling, experimentation, deployment, monitoring, and iteration.

• Create offline evaluations and online experiments to assess model performance and impact on clients and the business.

• Work with extensive behavioral and product datasets using Python, SQL, and distributed data-processing tools.

• Develop production-quality ML solutions that prioritize scalability, reliability, latency, observability, and cost-effectiveness.

• Partner with Product, Engineering, and Data Science teams to convert downstream needs into reusable foundational ML capabilities.

• Influence technical direction through design discussions, code reviews, research, prototyping, and best-practice development.

• Articulate technical concepts, modeling strategies, trade-offs, and recommendations to both technical and non-technical stakeholders.

• Mentor and collaborate with Data Scientists and engineers.

⛳️ Requirements

• Bachelor’s Degree in a quantitative discipline such as Computer Science, Statistics, Physics, Mathematics, or a related field is required.

• Over 8 years of experience in the design and deployment of machine learning solutions, preferably in personalization, including recommendation systems, representation learning, or search.

• Proficient in architecting technical solutions and writing production-quality code in Python.

• Capable of independently navigating ambiguous machine learning challenges from initial exploration and prototyping to production deployment, monitoring, iteration, and achieving measurable impact.

• Experience in handling large-scale datasets using SQL and distributed data-processing technologies like Spark.

• Familiarity with modern deep-learning frameworks such as PyTorch or TensorFlow.

• Strong grasp of model evaluation and experimentation techniques, including offline evaluation, A/B testing, and translating model enhancements into quantifiable product or business outcomes.

• Ability to assess trade-offs in production ML systems, including model quality, latency, scalability, reliability, and computational costs.

• Excellent communication and collaboration skills.

• A demonstrated intellectual curiosity and ability to learn and apply new machine learning techniques to real-world problems.

🏝️ Benefits

• Competitive salary.

• Equity.

• Annual bonus.

• New hire and ongoing grants of restricted stock units, contingent on employee and company performance.

• Medical benefits.

• Dental benefits.

• Vision benefits.

• Additional inclusive health and wellness benefits.

• Comprehensive compensation packages.

• A diverse and inclusive community.