Scientist Jobs

About The Position

Requirements

• Master’s degree in Computer Science, Operations Research, Statistics, Applied Mathematics, Engineering, or a related field; a suitable combination of education, experience, or training will also be accepted.
• At least 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 with machine learning frameworks such as PyTorch and TensorFlow.
• Deep knowledge of recommendation systems, ranking models, personalization, and predictive analytics.
• Experience designing and evaluating large-scale experimentation platforms and A/B testing methodologies.
• Experience building solutions using Agentic AI, large language models (LLMs), and 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, including the ability to lead scalable personalization solutions combining traditional recommendation methods, embeddings, semantic retrieval, ranking, and generative approaches.
• Experience with cloud-native ML platforms such as Databricks and AWS SageMaker.
• Demonstrated ability to lead complex cross-functional initiatives.
• Strong communication skills.

Nice To Haves

• Strong background in optimization techniques, including linear programming, mixed-integer optimization, reinforcement learning, and multi-objective optimization.

Responsibilities

• 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 with product managers, engineers, designers, and business stakeholders to define strategy, prioritize opportunities, and execute 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.