Who We Are

Wayfair’s Machine Learning team builds the algorithmic systems that drive our business, enhance customer experience, & improve customer loyalty. This ML team develops advanced machine learning and econometric models to power a range of high value initiatives for Wayfair: (1) deep catalog understanding to power tagging, comparisons of products and identification of high potential products in our catalog (2) drive pricing and profitability for all of Wayfair, and (3) drive efficiencies and improve outcomes across the Wayfair’s B2B and Sales programs. 

We are looking for an ML scientist to join the Global Supplier Tech Science and Analytics team, with an emphasis on developing scalable algorithms and platforms to better serve Wayfair’s customers. You will need to own the design, prototyping, implementation and governance of ML models. You will be working together with other data scientists, as well as multiple business and engineering teams to solve some of the most impactful and intellectually challenging data science and machine learning problems at Wayfair.

What You’ll Do

• Develop sophisticated and cutting-edge Machine Learning models using Wayfair’s product and customer data to power a wide range of company initiatives 
• Integrate model outputs into a variety of end use cases with partner business and engineering teams to drive value for Wayfair
• Work with partner engineering teams to build real-time models & services for widespread consumption across Wayfair
• Stay up-to-date on the latest ML research and appropriately utilize state-of the-art techniques in delivering solutions
• Drive the development, implementation, testing and governance of ML solutions
• Communicate results and influence decision making with senior stakeholders to drive improvements in end-to-end processes

We Are A Good Match Because You Have: 

• Advanced degree (PhD) in a quantitative field (e.g. mathematics, economics, computer science, engineering, physics, neuroscience, operations research, etc.) or 1-2 years of experience with a Masters in a quantitative field or 4+ years of experience with a Bachelors in a quantitative field
• Machine Learning using a wide range of techniques (such as supervised/weakly supervised learning, computer vision, deep learning, etc.)
• Proficient at one or more programming languages, e.g. Python, Java, C++, etc.
• Experience building scalable data processing pipelines with big data tools such as Hadoop, Hive, SQL, Spark, etc.
• Experience with cloud compute (i.e. GCP, AWS), schedulers (i.e. Airflow), and containerization (Docker) are nice to have
• Ability to effectively work with business leads: strong communication skills, ability to synthesize conclusions for non-experts and desire to influence business decisions
• A bias towards solving problems and an intuitive sense for how the work aligns closely with business objectives
• Ability to thrive in a dynamic environment where there can be varying levels of ambiguity

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