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Economist II

Join our Pricing team and play a crucial role in shaping Wayfair's pricing strategy! We're a science team with a mission to optimize long-term profitability by finding the perfect balance between short-term profitability and sustainable customer growth. We do this by leveraging economic theory, cutting-edge ML, econometrics, and live experimentation to ensure our pricing is competitive, fair to our suppliers, and drives customer engagement.

In this role, you'll be pushing the boundaries of demand estimation and causal inference by working with rich, high-volume customer data and ML tools. You'll use both observational and experimental methods to understand how customer short- and long-term price sensitivity shifts in response to various business decisions. Partnering with our engineering team, you'll implement these best-in-class approaches as scalable solutions that drive impact by improving Wayfair pricing strategies. 

What you’ll do

  • Lead R&D effort to develop innovative demand estimation approaches that will extend current capabilities and develop new ML products to power our pricing.
  • Understand our pricing strategy and optimization algorithm to identify what to measure and how to measure it.
  • Work highly cross-functionally with business leadership, stakeholders, and partner science and engineering teams to drive the integration of model outputs into company-wide business decision-making and existing production systems. 
  • Developing robust solutions to infer or recover causal relationships from observational data, in a way that minimizes self-selection or third-variable problems and reliance on experimental data. 
  • Develop and own communication strategy for both technical and business stakeholders (including executive documentation and progress updates)
  • Act as a subject matter expert and thought leader on demand estimation and causal inference techniques 
  • To stay ahead of the curve, you'll continuously research and evaluate new techniques in data science, machine learning, and pricing optimization.

Who you are

  • PhD in Economics, Statistics or other quantitative field with an emphasis on causal inference and/or structural modeling (e.g. industrial organization, structural labor, quantitative marketing, OR).
  • 2 years of experience working as a professional ML or research scientist in causal inference. Consistent track record of autonomous delivery of DS/ML projects that drive measurable business impact. 
  • Collaborative team player who wants to see themselves and others thrive. 
  • Expertise in causal inference and interest in bringing best-in-class solutions to e-commerce space. 
  • Takes a customer-centric approach to ideation and problem-solving
  • Track-record of influencing non-technical stakeholders on strategic direction by leveraging data-driven insights; excellent written and verbal communication.
  • Strategic thinker with a customer-centric mindset and a desire for creative problem solving, looking to make a big impact in a growing organization
  • Strong SQL skills and excellent software engineering skills in Python
  • Nice-to-haves:
  • Experience with GCP (BigQuery, GCS, Dataproc, Notebooks), Airflow, and containerization (Docker)
  • Experience building scalable data processing pipelines with big data tools such as Hadoop, Hive, Spark, etc.


Massachusetts Applicants: I understand that it is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.

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 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, genetic information, or any other legally protected characteristic.

Your personal data is processed in accordance with our Candidate Privacy Notice (https://www.wayfair.com/careers/privacy). If you have any questions or wish to exercise your rights under applicable privacy and data protection laws, please contact us at dataprotectionofficer@wayfair.com.

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What You Should Know About Economist II, Wayfair

If you’re passionate about economics and want to make a real impact, Wayfair is looking for you to join our team as an Economist II in Boston, MA! This pivotal role within our Pricing team offers the chance to shape Wayfair's pricing strategy while optimizing long-term profitability. You’ll be part of a dynamic science team that thrives on leveraging economic theory, machine learning, and econometrics to ensure our pricing remains competitive and fair to our suppliers. By utilizing both observational and experimental methods, you'll gain insights into how customer price sensitivity evolves with various business decisions. In collaboration with our engineering team, you’ll also translate these insights into scalable solutions that refine our pricing strategies. As an Economist II, you will lead research to advance innovative demand estimation methods, incorporating your expertise to enhance our current analytics tools. You’ll have the opportunity to work cross-functionally with diverse business leadership and stakeholders, guiding the integration of your findings into company-wide decision-making processes. If you’re a collaborative problem-solver with a PhD in Economics or a related field, this is your chance to leverage advanced causal inference techniques to revolutionize how we see pricing in e-commerce. At Wayfair, every voice matters and we value a diverse work environment that fosters creativity and innovation. If you want to pursue a rewarding career where you can grow rapidly and tackle fascinating challenges, we’d love to hear from you!

Frequently Asked Questions (FAQs) for Economist II Role at Wayfair
What are the responsibilities of an Economist II at Wayfair?

As an Economist II at Wayfair, you'll lead R&D efforts to innovate demand estimation techniques and create machine learning products to enhance pricing strategies. You will collaborate cross-functionally with business leaders, stakeholders, and engineering teams to integrate your findings into decision-making processes. Your role includes developing solutions to infer causal relationships from observational data while crafting a clear communication strategy for various audiences.

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What qualifications are needed to become an Economist II at Wayfair?

To qualify for the Economist II position at Wayfair, candidates typically need a PhD in Economics, Statistics, or a related quantitative field with a focus on causal inference or structural modeling. Additionally, having at least two years of experience as a machine learning or research scientist in causal inference is essential. Strong SQL skills and proficiency in Python are also highly desired.

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How does the role of Economist II contribute to Wayfair's pricing strategy?

The role of Economist II is crucial in shaping Wayfair's pricing strategy by using economic principles and data analysis to optimize profitability. This involves leading research and development efforts that focus on innovative demand estimation methods, ultimately informing the pricing algorithms used across the company to ensure competitiveness and customer satisfaction.

Join Rise to see the full answer
What skills are important for success as an Economist II at Wayfair?

Success as an Economist II at Wayfair requires a blend of analytical and collaborative skills. Proficiency in causal inference, strong problem-solving ability, and exceptional communication skills are key. Additionally, experience with data science tools and methodologies, alongside a customer-centric mindset, will significantly enhance your effectiveness in this role.

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What is the company culture like at Wayfair for an Economist II?

Wayfair fosters a vibrant company culture centered on innovation, collaboration, and inclusivity. As an Economist II, you’ll find yourself in an environment that encourages creative problem-solving and values diverse perspectives. The company is dedicated to providing learning opportunities and recognizing the contributions of its employees, making it a rewarding place to grow your career.

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Common Interview Questions for Economist II
Can you describe your experience with causal inference techniques?

When preparing for this question, focus on specific techniques you've applied in previous roles, such as regression analysis or randomized control trials. Explain how these methods helped you derive insights from data and guided business decisions in your past projects.

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How would you approach demand estimation for a new market?

Outline a step-by-step approach, such as conducting market research, analyzing historical sales data, and considering external factors that might influence demand. Emphasize your ability to leverage ML techniques to refine your estimates as you gather more data.

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Describe a challenging project you've completed related to ML or data science.

Share a specific example of a complex project that required innovative thinking or problem-solving. Highlight the methods used, the challenges faced, and the measurable impacts your work had on the organization.

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How do you ensure your models are robust and accurate?

Discuss the techniques used to validate your models, such as cross-validation, testing against benchmarks, or continuous monitoring of performance after deployment. This shows your commitment to producing high-quality, reliable results.

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What is your experience collaborating with non-technical stakeholders?

Provide examples of how you've effectively communicated complex technical concepts to business leaders. Illustrate the importance of storytelling with data and how you've influenced strategic decisions based on your analyses.

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What tools or software do you prefer for data analysis?

Mention your proficiency in key tools relevant to the role, such as Python, SQL, or any cloud platforms like GCP. Explain how these tools have enhanced your ability to analyze data and support pricing strategies.

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How do you prioritize competing research projects?

Explain your method for assessing project importance based on potential business impact, resource availability, and deadlines. Highlight your organizational skills and the ability to adapt as project priorities change.

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What is your approach to testing hypotheses in pricing strategies?

Discuss your familiarity with A/B testing or other experimental designs used to test various pricing strategies. Emphasize your analytical mindset and your commitment to using data to drive pricing decisions.

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Can you provide an example of a time you influenced a pricing decision?

Detail a specific instance where your data-driven insights led to a tangible change in pricing strategy. Articulate the impact this change had on sales, customer engagement, or profitability.

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What keeps you motivated in a data-driven role?

Share your genuine passion for data analysis and the insights it can provide. Reflect on the thrill of solving complex problems and the satisfaction derived from seeing your work drive real business outcomes and customer satisfaction.

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April 13, 2025

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