Data Science Tech Lead, Experimentation and Statistical Inference

Boston, MA

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Wayfair Inc.

Shop Wayfair for A Zillion Things Home across all styles and budgets. 5,000 brands of furniture, lighting, cookware, and more. Free Shipping on most items.

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Apply now Apply later is a leader in the e-commerce space for all things home. Wayfair’s community of Data Scientists are obsessed with using data and technology to ensure our customers can build a home that they love. Our platforms support millions of people searching every day for the perfect item, and provide a unique purchase and delivery experience. Come help us innovate and grow to our next $10B in revenue!   

We are looking for an experienced statistician or machine learning engineer interested in partnering with data scientists, product managers, engineers, and cross-functional business partners (e.g. marketing, website, B2B, operations, merchandise, pricing) to drive Wayfair’s state-of-the-art experimentation platform. This includes but is not limited to the intricacies involved in operationalizing and scaling advanced statistical and machine learning models to power all experiments at Wayfair and researching new statistical and causal inference techniques to extract insights more accurately and granularly.

We’re looking for someone who not only enjoys statistics, writing code and building models in production, but is also able to upskill engineers, data scientists, and business leaders around them through leading by example. In this role, you will be driving cutting-edge statistical and machine learning research to continuously increase the accuracy and robustness of testing methodologies, reduce the cost of running experiments, and support optimal decision making and investment for everything Wayfair does. 

What You'll Do

  • Lead the research and development of Statistical/Machine Learning (ML) models and pipelines to improve the accuracy and efficiency of running experiments at Wayfair.
  • Build scalable experimentation products by developing python packages, leveraging Google Cloud native technologies, and driving platform integrations via microservices.
  • Champion open source solutions and/or develop novel approaches to unlock new testing capabilities to answer complex business problems, such as measuring customer life-time value, heterogeneous treatment effects, and marketing elasticity.
  • Build and run large-scale simulations to optimize, validate, and promote your models and methods.
  • Think outside of the current technology/stack limitations to push the boundaries on what is possible and deliver feasible solutions collaboratively.
  • Promote a culture of data science excellence and strengthen the testing rigor.

What You'll Need

  • A Ph.D. degree in Statistics, Biostatistics, Econometrics, or a related quantitative field.
  • 1+ years of experience in advanced statistical modeling, machine learning, or reinforcement learning (e.g. multi-armed bandits) including hands-on designing and building production models at scale.
  • Experience developing innovative statistical methodologies that tackle real-world experimentation challenges (e.g. longitudinal/heterogeneous effects, autocorrelation, skewed/sparse data, latent confounders).
  • Familiarity with ML model development frameworks, ML orchestration and pipelines with experience in either Airflow, Kubeflow or MLFlow as well as Spark and python.
  • Excellent organizational, analytical, and hypothesis- driven critical thinking skills to identify business opportunities and transform data into actionable insights.
  • Excellent communication skills to explain complex statistical and data science concepts/ideas/methods to technical and business audiences.

Nice to Have

  • Mix of start-up and large-company experience working on experimentation, advanced statistical modeling, and machine learning.
  • Familiarity with Machine Learning platforms offered by Google Cloud and how to implement them on a large scale.
  • Direct experience leading research around experimental design, machine learning for variance reduction, mixed effect models, Bayesian hierarchical models, or computational statistics.

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.

Job perks/benefits: Career development
Job region(s): North America
Job stats:  2  0  0
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