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 aimed at enhancing personalization and recommendation systems.

• Enhance foundational modeling capabilities, which include client and item representations, embeddings, retrieval, ranking, recommendation models, and assortment generation.

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

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

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

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

• Develop scalable, dependable, and observable production ML solutions that are cost-efficient.

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

• Influence technical direction through design discussions, code reviews, research, prototyping, and the establishment of best practices.

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

• Mentor and work alongside Data Scientists and engineers.

⛳️ Requirements

• A 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 designing and deploying machine learning solutions, particularly in personalization, including recommendation systems, representation learning, or search.

• Strong capability to architect technical solutions and produce production-ready code in Python.

• Ability to autonomously navigate complex machine learning challenges from initial exploration and prototyping through to production deployment, monitoring, iteration, and demonstrable impact.

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

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

• Profound understanding of model evaluation and experimentation, encompassing offline evaluation, A/B testing, and translating model enhancements into quantifiable product or business results.

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

• Excellent communication and collaboration abilities, with a knack for influencing technical decisions and articulating complex concepts to both technical and non-technical audiences.

• A keen intellectual curiosity and a proven capacity to learn and apply new machine learning techniques to real-world problems.

🏝️ Benefits

• Competitive salary.

• Eligibility for annual bonuses.

• Restricted stock unit grants for new hires and ongoing grants based on employee and company performance.

• Medical benefits.

• Dental benefits.

• Vision benefits.

• Additional inclusive health and wellness benefits.

• Comprehensive compensation package.

• Equity opportunities.

• A diverse and inclusive workplace.

• A growth-oriented work environment.