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 to enhance personalization and recommendation systems.

• Enhance core modeling capabilities, focusing on client and item representations, embeddings, retrieval, ranking, recommendation models, and assortment generation.

• Investigate and utilize large language models (LLMs), deep learning, representation learning, multimodal modeling, and generative techniques.

• Manage the complete 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 along with client and business impact.

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

• Develop production-ready ML solutions with a focus on scalability, reliability, latency, observability, and cost efficiency.

• 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 adherence to best practices.

• Articulate technical concepts, modeling strategies, tradeoffs, 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 area is required.

• Over 8 years of experience in designing and deploying machine learning solutions, preferably in personalization, recommendation systems, representation learning, or search.

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

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

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

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

• Strong comprehension of model evaluation and experimentation, including offline evaluation, A/B testing, and translating model enhancements into measurable product or business results.

• Ability to analyze production ML system tradeoffs, covering model quality, latency, scalability, reliability, and computational cost.

• Excellent communication and collaboration skills, capable of influencing technical decisions and conveying complex ideas to both technical and non-technical audiences.

• A strong intellectual curiosity with a proven track record of learning and applying new machine learning methods to real-world problems.

🏝️ Benefits

• Competitive salary.

• Equity opportunities.

• Annual bonus potential.

• New hire and ongoing restricted stock unit grants based on individual and company performance.

• Comprehensive medical benefits.

• Dental insurance.

• Vision coverage.

• Additional inclusive health and wellness benefits.

• A thorough compensation package.