: Data Science Tech Lead - Catalog

Who We Are

Wayfairs Data Science team builds the algorithmic systems that drive our business, enhance customer experience, & improve customer loyalty. The Data Science Catalog team at Wayfair develops machine learning models to: (1) improve customer experience by accurately tagging products using multimodal data sources (image, text, categorical, numeric, etc.), (2) drive catalog and pricing strategy by powering comparisons between large numbers of products, and (3) solve the product cold-start problem to enable customers to find the best new products.

We partner closely with our Merchandising, Engineering, and other Data Science teams in order to build scalable algorithmic systems responsible for predictive modeling that drives business strategy for various merchandising and supplier side teams. 

We are looking for a technical lead to join the Data Science Catalog team, with an emphasis solving ambiguous machine learning problems to better identify the best new SKUs in Wayfairs marketplace. You will be working together with other data scientists, as well as multiple merch and engineering teams to solve one of our most impactful and intellectually challenging data science problems at Wayfair. 

What You'll Do

  • Develop quantitative models using multiple types of data (image, text, categorical and numeric data), leveraging machine learning and advanced data analysis techniques
  • Architect and build technical platforms for our algorithmic engines to run at scale
  • Leverage our work in order to increase adoption across our business partners, to drive real business value
  • Work with partner engineering teams to build real-time models & services for company-wide consumption 
  • Uncover deep insight hidden in our vast repository of raw data, and provide tactical guidance on how to act on findings
  • Deliver presentations to high level business stakeholders that tell cohesive, logical stories using data

What You'll Need

  • 4+ years of experience in a quantitative or technical work environment or advanced degree (PhD) in quantitative field (e.g. mathematics, economics, computer science, engineering, physics, neuroscience, operations research, etc.)
    Machine Learning experience (such as supervised/unsupervised learning, recommendation systems, reinforcement learning, deep learning, etc.)
  • Proficient at one or more programming languages, e.g. Python, R, Java, C++, etc.
  • Prior experience building scalable data processing pipelines with big data tools such as Hadoop, Hive, SQL, Spark, etc.
  • Experience with GCP, Airflow, and containerization (Docker) are nice to have
  • A bias towards solving problems from a customer-centric lens and an intuitive sense for how the work aligns closely with business objectives
  • Ability to effectively work with business leads: strong communication skills, ability to synthesize conclusions for non-experts and desire to influence business decisions

About Us:

Wayfair is one of the worlds largest online destinations for the home. Whether you work in our global headquarters in Boston or Berlin, or in our warehouses or offices throughout the world, were 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 youre looking for rapid growth, constant learning, and dynamic challenges, then youll find that amazing career opportunities are knocking.

No matter who you are, Wayfair is a place you can call home. Were 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. Thats why were 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, or genetic information.

Full-time