Data Science Tech Lead, Forecasting

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about 2 years old

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The Team

We’re looking for a Senior Data Scientist, ideally with a background in time series forecasting, to join the Forecasting Science team at Wayfair. The Forecasting Science team consists of data scientists, economists, and machine learning engineers responsible for owning large scale systems and driving forecasting best practices throughout the business, consulting with both science and business teams. We develop econometric, statistical, machine learning, and hybrid forecasting models at massive scale to drive operational excellence throughout our supply chain from item level demand for our millions of products, to staffing needs for our growing warehouse network, to container demand for our international shipping network. 

As a Senior Data Scientist you should be eager to partner closely with our business partners to drive our rapidly scaling operations, while being part of a collaborative and curious scientific community looking to dive deep into state of the art forecasting techniques leveraging both statistics and machine learning in concert. It’s an exciting time to join our team both for the business impact you’ll be able to drive, and the techniques you’ll be able to develop as machine learning propagates through the time series forecasting frontier. 

What You’ll Do 

  • Design and implement
    • Statistical, machine learning, and hybrid time series forecasting models, particularly within a hierarchical context 
    • Metrics highly correlated with business performance
    • Unsupervised learning algorithms 
    • Robust and scalable data pipelines 
  • Scope concrete solutions to ambiguous business problems
  • Collaborate with business partners, data engineers, data scientists, economists, and machine learning engineers to drive end-to-end success of your solutions
  • Communicate the value of your work to a broad audience, both business and technical
  • Mentor and empower junior data scientists

What You’ll Need

  • Masters Degree and 3+ years of experience or Ph.D. and 1+ years of experience in a quantitative subject (i.e. computer science, economics, operations research, statistics, etc.)
  • Proficiency at either Python or R and willingness to engage with both languages
  • Experience building time series forecasting models leveraging machine learning, statistical, econometric, and/or hybrid models
  • A genuine interest in the happiness, well-being, and success of everyone on your team
  • The ability to learn fast, a willingness to work hard, integrity, compassion, and a team-first attitude
  • The ability to work with business and technical contributors: strong verbal and written communication skills, the ability to synthesize conclusions for non-experts, and the desire to influence business decisions
  • Prior experience working with large datasets, leveraging tools like Spark, Hive, Airflow and experience with Google Cloud Platform is a plus.

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 Berlin, 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, or genetic information.