Machine Learning Scientist I

About the Role

Wayfair is looking for candidates passionate about programming with strong core fundamentals. This is a Boston-based opportunity with a 3-day in-office expectation (Tuesday - Thursday) and 2 days remote (Monday and Friday).

Key Responsibilities

• Own the full Data Science/Machine Learning lifecycle from conception to prototyping, testing, deploying, and measuring its overall business value.
• Develop and scale state-of-the-art machine learning methods and quantitative models to address core business problems.
• Integrate algorithmic solutions into technical platforms to run at scale and directly change customer experiences.
• Drive measurable business value by collaborating with business teams.
• Pilot projects using new open-source tools and packages to enable novel machine learning techniques.
• Uncover deep insights from raw data and provide tactical guidance on how to act on findings.
• Use data to improve employee decision-making, and ultimately, enhance customer and supplier experiences.
• Collaborate with ML Scientists and software engineers to create maintainable, scalable, and debuggable code by bringing strong software development practices.
• Drive potential scientific publications based on results in major Data Science and Machine Learning conferences or workshops.

Requirements & Tech Stack

• Proficient knowledge of structured programming language(s), such as Python.
• Experience with languages and tools relevant to data science, including Pytorch/TF, Github, Pyspark, Docker, SQL, GCP.
• 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.
• Familiarity with regression and machine learning techniques, such as supervised/unsupervised learning, recommendation systems, reinforcement learning, deep learning.
• An affinity for data along with experience leveraging statistics and regression analysis.
• Intuitive sense of how quantitative and technical work aligns closely with business priorities and business value.
• Familiarity with data structures, algorithms, OOP, and programming in a team environment.
• Quick learner with an analytical approach to solving problems as part of a team who has strong communication skills.
• Ability to thrive in a dynamic environment where there can be degrees of ambiguity.
• Ability to effectively work with technical leads: ability to synthesize conclusions for non-experts and desire to influence technical decisions.
• 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.
• 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.
• Proven track record of delivering successful machine learning projects from conception to production, demonstrating strong deployment, problem-solving, and maintenance skills.
• Deep understanding of data engineering concepts with experience in building scalable data pipelines for collecting, processing, and transforming data.

Qualifications

• 1-3 years of transferable experience in a corporate environment, including internship and co-op experience.
• Masters degree in a quantitative field (Mathematics, Science, Engineering, Computer Science, Statistics/Biostatistics, Economics, Physics, Robotics, Operations Research, etc.).