Lead Data Scientist

We are looking for a Lead Data Scientist to advance state-of-the-art recommendation systems. This role sits at the intersection of machine learning, optimization, generative AI, and decision intelligence that help customers and partners discover the right products, optimize assortments, and make informed purchasing decisions. You will lead the development of innovative recommendation solutions that connect customers and partners with the most relevant products, using machine learning, optimization, and AI-assisted workflows to create personalized, data-driven experiences at scale.

You are passionate about solving complex business problems, translating ambiguous requirements into production-ready systems, and leading cross-functional teams through innovation.

You thrive in environments where data science, AIML engineering, product management, and user experience come together to create impactful customer experiences.

• Master's degree in Computer Science, Operations Research, Statistics, Applied Mathematics, Engineering, or a related field. Will accept any suitable combination of education, experience or training
• 5+ years of experience leading the design, development, and deployment of data science, machine learning, optimization, and operations research models at scale
• Strong proficiency in Python and experience leveraging machine learning frameworks such as PyTorch, Tensor Flow
• Deep knowledge of recommendation systems, ranking models, personalization, and predictive analytics
• Experience designing and evaluating large-scale experimentation platforms and A/B testing methodologies
• Strong background in optimization techniques, including linear programming, mixed-integer optimization, reinforcement learning, and multi-objective optimization is preferred
• Experience building solutions using Agentic AI, Large Language Models (LLMs), tool orchestration and evaluating Agentic systems using automated and human-in-the-loop approaches
• Demonstrated expertise in modern recommender systems and emerging LLM/GenAI techniques, with the ability to lead the design of scalable personalization solutions combining traditional recommendation methods, embeddings, semantic retrieval, ranking, and generative approaches.
• Experience with cloud-native ML platforms such as Databricks, AWS Sage Maker
• Demonstrated ability to lead complex cross-functional initiatives
• Strong communication skills

As a Lead Data Scientist, you will lead the scientific strategy and technical execution for AI-powered recommendations. Your work will span predictive modeling, recommendation systems, optimization engines, and generative AI experiences.

Key responsibilities include:

• Develop and deploy advanced recommendation models that deliver personalized product experiences aligned with customer needs and partner business objectives
• Design and implement optimization frameworks that balance customer preferences, inventory availability, profitability, and operational constraints
• Lead the development of generative AI capabilities that enhance recommendation experiences through intelligent insights, explanations, and decision support
• Establish experimentation and measurement frameworks to evaluate recommendation quality, customer engagement, business impact, and AI effectiveness
• Partner closely with product managers, engineers, designers, and business stakeholders to define strategy, prioritize opportunities, and execute against the product roadmap
• Drive scientific rigor through model evaluation, offline benchmarking, online experimentation, and continuous improvement
• Mentor and develop data scientists and machine learning engineers, fostering technical excellence and a culture of learning
• Communicate complex analytical findings and strategic recommendations to technical and non-technical audiences, including senior leadership

We offer a number of accommodations to complete our interview process including screen readers, sign language interpreters, accessible and single location for in-person interviews, closed captioning, and other reasonable modifications as needed.