Data Scientist Jobs

About The Position

Requirements

• Bachelor’s degree in a quantitative field such as Computer Science, Statistics, Physics, Mathematics, or a related field.
• At least 8 years of experience designing and deploying machine learning solutions, ideally in personalization, such as recommendation systems, representation learning, or search.
• Strong ability to architect technical solutions and write production-grade Python code.
• Ability to independently drive ambiguous machine learning problems from exploration and prototyping through production deployment, monitoring, iteration, and measurable impact.
• Experience working with large-scale datasets using SQL and distributed data-processing technologies such as Spark.
• Strong understanding of modern deep-learning frameworks such as PyTorch or TensorFlow.
• Strong understanding of model evaluation and experimentation, including offline evaluation, A/B testing, and translating model improvements into measurable product or business outcomes.
• Ability to reason about production ML tradeoffs, including model quality, latency, scalability, reliability, and computational cost.
• Strong communication and collaboration skills, including the ability to influence technical decisions and communicate complex concepts to technical and non-technical stakeholders.
• Intellectual curiosity and demonstrated ability to learn and apply new machine learning techniques to practical problems.

Nice To Haves

• Master’s degree or PhD.
• Experience in personalization, including recommendation systems, representation learning, or search.

Responsibilities

• Design, develop, evaluate, and productionize machine learning models that improve personalization and recommendation systems.
• Advance foundational modeling capabilities, including client and item representations, embeddings, retrieval, ranking, recommendation models, and assortment generation.
• Explore and apply LLMs and machine learning techniques, including deep learning, representation learning, multimodal modeling, and generative approaches, where they can meaningfully improve systems.
• Own work across the full ML lifecycle, from problem formulation and data exploration through modeling, experimentation, deployment, monitoring, and iteration.
• Design rigorous offline evaluations and online experiments to assess model performance, measure client and business impact, and communicate results to stakeholders and leadership.
• Work with large-scale behavioral and product datasets using Python, SQL, and distributed data-processing tools.
• Build production-quality ML solutions with attention to scalability, reliability, latency, observability, and cost.
• Collaborate with Product, Engineering, and other Data Science teams to understand downstream needs and develop reusable foundational ML capabilities.
• Contribute to the technical direction of the Foundational Models team through design discussions, code reviews, research, prototyping, and development of engineering and modeling best practices.
• Clearly communicate technical concepts, modeling approaches, tradeoffs, and recommendations to technical and non-technical partners.
• Mentor and collaborate with Data Scientists and engineers, helping raise the team’s technical bar.

Benefits

• Comprehensive compensation packages.
• Medical, dental, vision, and other benefits.
• Eligibility for an annual bonus.
• Eligibility for new-hire and ongoing restricted stock unit grants, depending on employee and company performance.