Machine Learning Scientist II - Demand Forecasting Science

About this role:

We are seeking a strong, action-oriented Machine Learning Scientist to develop, refine, and deploy innovative solutions for Wayfair's Demand Forecasting Science team. In this highly impactful role you will apply cutting-edge deep learning and statistical models to solve mission critical problems in demand, supply-chain forecasting and recommender systems. Success in this role requires proficiency in deep learning models and techniques, strong problem solving and communication skills, customer obsession, and the ability to work effectively across cross-functional teams. The ideal candidate also has experience with recommendation systems and/or in forecasting or time series analysis.

What You'll do:

• Research and experiment with state-of-the-art deep learning/supervised learning techniques and algorithms. Design and implement evaluation strategies applied to real-world scenarios tailored to Wayfair use cases in forecasting or recommendations.
• Identify new opportunities and insights from the data (where can the models be improved? what is the projected ROI of a proposed modification?)
• 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.
• Work with product managers and commercial stakeholders to understand business problems or opportunities and develop appropriately scoped analytical solutions.
• Partner with cross-functional teams across engineering, science, and product to ensure our solutions integrate seamlessly into our forecasting and recommender systems.
• Be obsessed with the customer and maintain a customer-centric lens in how we frame, approach, and ultimately solve every problem we work on.
• Stay current with the latest research in statistical, forecasting, and deep learning techniques and models as they apply to problems in recommendation and forecasting.

Who you are:

• PhD with 0-1+ years of experience or Master's in Computer Science, Machine Learning, or a related quantitative field with 2+ years of full-time industry experience in applied research.
• Proficiency in Python or one other high-level programming language; skilled in using ML frameworks (such as TensorFlow, PyTorch), and version control best practices.
• Must have a strong theoretical understanding and solid hands-on expertise deploying supervised learning or deep learning solutions into production.
• Deep understanding of data engineering concepts with experience in building scalable data pipelines for collecting, processing, and transforming data.
• Strong written and verbal communication skills, ability to synthesize conclusions for non-experts, and to effectively collaborate across teams, customer obsession, and overall bias towards simplicity.
• 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.

Nice to have:

• Research publications in leading conferences and journals in relevant fields such as deep learning, statistics, forecasting, or econometrics.
• Experience working in e-commerce recommendation systems.
• Experience in demand/supply-chain forecasting.
• Experience with Google Cloud Platform (or AWS, Azure), and ML orchestration tools such as Airflow and Kubeflow.

Why You'll Love Wayfair:

• Time Off:
• Paid Holidays
• Paid Time Off (PTO)
• Health & Wellness:
• Full Health Benefits (Medical, Dental, Vision, HSA/FSA)
• Life Insurance
• DIsability Protection (Short Term & Long Term DIsability)
• Global Wellbeing: Gym/Fitness discounts (including US Peloton, Global ClassPass, and various regional gym memberships)
• Mental Health Support (Global Mental Health, Global Wayhealthy Recordings)
• Caregiver Services
• Financial Growth & Security:
• 401K Matching (Employee Matching Program)
• Tuition Reimbursement
• Financial Health Education (Knowledge of Financial Education - KOFE)
• Tax Advantaged Accounts
• Family Support:
• Family Planning Support
• Parental Leave
• Global Surrogacy & Adoption Policy
• Professional Development & Recognition:
• Rewards & Recognition
• Global Employee Anniversary Awards
• Paid Volunteer Work
• Unique Perks:
• Employee Discount
• U.S. Bluebikes Membership
• Global Pod Outings
• Work/Life Balance:
• Emphasizing a supportive & flexible work environment that encourages a balance between personal and professional commitments

If you don't meet every qualification listed, we still encourage you to apply. We're looking for strong team players who can learn, grow, and make an impact.

This is a hybrid position and requires employees in-office Tuesday, Wednesday, Thursday and remote on Monday and Fridays.

About Wayfair Inc.

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.

No matter who you are, Wayfair is a place you can call home. We're a community of innovators, risk-takers, and trailblazers who celebrate our differences, and know that our unique perspectives make us stronger, smarter, and well-positioned for success. We value and rely on the collective voices of our employees, customers, community, and suppliers to help guide us as we build a better Wayfair - and world - for all. Every voice, every perspective matters. That's why we're proud to be an equal opportunity employer. We do not discriminate on the basis of race, color, ethnicity, ancestry, religion, sex, national origin, sexual orientation, age, citizenship status, marital status, disability, gender identity, gender expression, veteran status, genetic information, or any other legally protected characteristic.

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