Applied Research Scientist

Applied Research Scientist

Requirements

• PhD with 1+ years of experience or Master’s in Computer Science, Data Science, Machine Learning, or a related quantitative field with 3+ years of industry experience in applied research
• Deep understanding of data engineering concepts with experience in building scalable data pipelines for collecting, processing, and transforming data
• Proven track record of delivering successful machine learning projects from conception to production, demonstrating strong deployment, problem-solving, and maintenance skills
• Professional coding expertise in languages like Python and R, proficiency in SQL, and experience with data visualization tools; skilled in using ML frameworks (TensorFlow, PyTorch) and implementing CI/CD, containerization, and version control best practices
• Hands-on experience with large language models (e.g., GPT, BERT, Transformers) and Retrieval-Augmented Generation (RAG) techniques, and fine-tuning LLM models to address specific business challenges
• Solid understanding of natural language processing methods and traditional machine learning techniques to effectively tackle diverse problems
• Excellent communication skills, with the ability to clearly articulate complex AI concepts to non-technical stakeholders while collaborating across teams
• Demonstrated ability to quickly learn new tools and techniques in a fast-paced, evolving environment, while managing multiple priorities with a high level of attention to detail and staying current with the latest ML research.

Responsibilities

• Research and experiment with state-of-the-art generative AI techniques and algorithms, evaluating their performance on benchmark datasets and real-world scenarios
• Conduct exploratory data analysis to uncover data patterns, relationships, and key features for model training
• Develop and deploy machine learning models in production by collaborating with software engineers and using robust CI/CD practices; ensure these models are scalable, secure, and continuously monitored for performance with effective troubleshooting
• Contribute to architectural and code review discussions to enhance our engineering ecosystem
• Develop, fine-tune, and implement GenAI models to improve customer service interactions, such as smart chatbots and agentic AI tools, ensuring they align with Wayfair’s data and context
• Apply advanced prompt engineering and domain-specific fine-tuning to boost AI model response relevance and performance in customer service scenarios, using experiments to benchmark improvements
• Collaborate with product managers, customer service operations, data engineers, and other stakeholders to translate business needs into technical solutions that integrate smoothly into agent workflows
• Develop key success metrics and build evaluation frameworks to regularly assess model output quality, using data-driven insights to continuously enhance AI capabilities
• Stay current with the latest research in GenAI, transformer architectures, and agentic AI, proactively experimenting with new techniques and tools to champion ideas that enhance Wayfair’s customer service experience.

Preferred Qualifications

• Experience with agentic AI systems and LLMOps principles for model lifecycle management, performance evaluation, and automated testing
• A PhD in AI or a strong publication record in top conferences and journals in the field.