Machine Learning Scientist III - Recommendations

Machine Learning Scientist

Recommender systems (e.g., image and content recommendations, homepage and email optimization and personalization) to enhance the customer experience at Wayfair. Your work will directly impact how millions of customers discover and engage with products, driving significant business value.

What You'll Do

• Develop and optimize recommendation models that power personalized experiences across Wayfair's site, app, email, and push notifications.
• Conduct applied research to improve recommender systems using traditional ML techniques, deep learning and reinforcement learning.
• Build scalable ML pipelines for training, evaluation, and inference, ensuring models operate efficiently in production.
• Work closely with engineering teams to deploy models in a production environment, addressing real-world constraints such as latency, interpretability, and scalability.
• Analyze model performance and iterate based on A/B test results, offline evaluation metrics, and business impact.
• Leverage and contribute to open-source ML frameworks while staying up to date with cutting-edge research in recommendation systems.
• Drive innovation by identifying opportunities to improve personalization strategies and developing novel algorithms that enhance customer engagement.
• Collaborate with cross-functional teams including product managers, software engineers, and data scientists to align ML objectives with business goals.
• Mentor other less experienced scientists on the team

Who You Are

• 5+ years of experience developing and deploying machine learning models, with a focus on recommendations, ranking, or personalization.
• Strong theoretical understanding of machine learning and deep learning applied to large-scale recommendation problems.
• Experience in training, evaluating, and optimizing recommendation models in production, leveraging techniques such as collaborative filtering, sequence modeling, representation learning and multi-armed bandits.
• Proficiency in Python and experience with ML frameworks such as TensorFlow, PyTorch, or Scikit-Learn.
• Familiarity with big data processing (Spark, Hadoop) and ML pipeline orchestration (Airflow, Kubeflow, MLflow).
• Strong coding skills and familiarity with building scalable ML systems in cloud environments (AWS, GCP, Azure).
• Ability to design experiments and analyze results using A/B testing and statistical techniques.
• Excellent communication skills, with the ability to explain complex ML concepts to non-technical stakeholders and drive data-driven decisions.

Nice To Have

• Experience developing core recommendation systems for eCommerce, marketplaces, or streaming platforms.
• Familiarity with reinforcement learning or contextual bandits for adaptive recommendation strategies.

This role offers the opportunity to work on high-impact ML problems at scale, shaping the future of personalization and recommendations at Wayfair. If you're passionate about building intelligent systems that enhance customer experiences, we'd love to hear from you!

Wayfair's In-Office Policy:

All Mountain View-based interns, co-ops, and corporate employees will be in office in a hybrid capacity. Employees will work in the office on designated days, Tuesday, Wednesday, and Thursday, and work remotely the other 2 days of the week.

Wayfair is one of the world's largest online destinations for the home. Whether you work in our global headquarters in Boston, or in our warehouses or offices throughout the world, we're reinventing the way people shop for their homes. Through our commitment to industry-leading technology and creative problem-solving, we are confident that Wayfair will be home to the most rewarding work of your career. If you're looking for rapid growth, constant learning, and dynamic challenges, then you'll find that amazing career opportunities are knocking.

Your personal data is processed in accordance with our Candidate Privacy Notice. If you have any questions or wish to exercise your rights under applicable privacy and data protection laws, please contact us at View email address on click.appcast.io.