The Team

We are looking for a Principal Economist with a background in causal inference to join the Central Algorithms group at Wayfair. Central Algorithms is a team of data scientists and economists that solve an array of challenging problems focused around short and long-term impacts of supply chain management (e.g. shipping speeds, late deliveries); lifts from merchandising actions in high-dimensional, noisy data (e.g. additional review or imagery); and machine learning models to predict and manage products with high damage rates. We partner directly with several business teams to understand each problem space deeply, identify appropriate and efficient statistical solutions, and embed the resulting models into business decision-making and/or algorithms.

Wayfair is a Fortune 500 technology company, selling home goods (including furniture), primarily online. As an economist, you will be joining us at an exciting moment in the company’s growth and help to solidify our internal economics culture. The existing group (~15 economists across Wayfair) provides a powerful community of colleagues, while still allowing you to have an outsized opportunity for impact at the company level and visibility to senior leaders across the organization. You will also work with non-economist data scientists and engineers, sharing a range of perspectives on how to best approach problems. In contrast to other established econ in tech roles, you will likely be the first economist working on a particular problem or within a business vertical, starting projects from scratch rather than iterating on a versioned release of a legacy model. 

Culturally, we value strong collaboration across diverse backgrounds and perspectives to make better decisions. We deliver impact incrementally while focusing on long-term goals. We seek and provide honest, empathetic feedback and always strive to improve. 

What You’ll Do

• Causally estimate the effectiveness of various business levers using both observational and experimental data (including designing and overseeing experiments). This often requires applying cutting-edge methods from the frontier of academic literature in causal inference. 
• Work with business partners to prioritize high-impact projects. Translate broad business requests into actionable science project plans that may involve many scientists. 
• Work closely with partner business teams to advise on how causal estimates should be integrated into systems and decision-making. 
• Communicate the value of your work (and rigorous causal inference in general) to a broad audience, both business and technical. 
• Work closely with engineering partners to build robust data pipelines and APIs required to update estimates periodically and surface causal estimates to other teams in the appropriate format. 
• Contribute to the economics and data science culture at Wayfair, including mentoring junior scientists, participating in internal journal clubs, providing feedback in design reviews, and participating in conferences. 

What You’ll Need

• Ph.D. in economics or another field performing quantitative causal analyses with a modern toolkit (quantitative marketing, political science, applied statistics, etc). 
• 3+ years of experience in causal inference in industry, consulting, or academia. We also welcome applications from economists with less experience, whom we’ll consider for our economist and senior economist job openings.
• Strong intuition about causal inference, applied econometrics, and applied microeconomics, including a wide and deep applied econometric toolkit. Some experience with machine learning is a plus.
• Ability to effectively work with business leads and data scientists, including strong verbal and written communication skills, ability to synthesize conclusions for non-experts, and desire to influence business decisions.
• Genuine interest in collaborating and mentoring other scientists on the team and across Wayfair. 
• Experience implementing models in Python is a plus.
• Prior experience working with large datasets, leveraging tools like Spark, Hive, Airflow and experience with Google Cloud Platform is a plus.