Product Data Analyst, Consumer

Who we are

At CarGurus (NASDAQ: CARG), our mission is to give people the power to reach their destination. We started as a small team of developers determined to bring trust and transparency to car shopping. Since then, our history of innovation and go-to-market acceleration has driven industry-leading growth. In fact, we’re the largest and fastest-growing automotive marketplace, and we’ve been profitable for over 15 years.

What we do

The market is evolving, and we are too, moving the entire automotive journey online and guiding our customers through every step. That includes everything from the sale of an old car to the financing, purchase, and delivery of a new one. Today, tens of millions of consumers visit CarGurus.com each month, and ~30,000 dealerships use our products. But they're not the only ones who love CarGurus—our employees do, too. We have a people-first culture that fosters kindness, collaboration, and innovation, and empowers our Gurus with tools to fuel their career growth. Disrupting a trillion-dollar industry requires fresh and diverse perspectives. Come join us for the ride!

Role Overview

We are looking for a Product Data Analyst to join our Consumer team which tackles a complex data environment, building out data assets, running experiments, and many more unstructured analytics tasks to support the core listings business.

Our Product Data Analytics org as a whole supports our Product and Engineering teams, providing the final word on all analytics for CarGurus’ user and dealer experiences. We are looking for thoughtful, curious, internally driven candidates who can dive into complex data and draw novel, insightful conclusions.

What you’ll do

• Using your SQL expertise, conduct exploratory empirical analyses that bridge disparate data sources (e.g. website usage, subscription records, inventory volumes, etc.) to quantify product performance, user behavior, and/or market trends. Doing so will require distilling unstructured “big data” into actionable insights.
• Creatively compress sweeping, high-level exploratory requests into specific calculations that address your stakeholders’ underlying needs.
• Relentlessly dig into the data – consulting with other individuals and teams as you judge necessary – beyond the letter of the initial assignment, to hammer out any anomalies or self-direct your inquiry into other relevant issues.
• Be comfortable escalating and automating your analyses using R/Python scripting.
• Be the authority on A/B experimentation, advising engineers and product managers on everything from the necessary data points to collect, required sample sizes, optimal metrics to examine, robustness of numerical findings, and the bottom-line success or failure of the tested changes.
• Provide technical guidance on and ideas for improving A/B testing-related tools, algorithms, and automated processes.
• Conduct self-directed research on the latest trends in A/B experimentation, and map out internal improvements based on your findings.
• Advocate for specific, data-driven product innovations that help further high-level company strategy, primarily in partnership with the Product/Engineering teams.
• Participate in brainstorming and planning discussions across the organization to these ends.
• Avoid passivity in the face of flawed proposals; tactfully and persuasively push back against potential missteps.
• Craft the metrics that define business success, condensing abstract or loosely-defined concepts down to concrete calculations.
• Audit and improve existing metrics to better inform the business’ needs.
• Build intuitive dashboards and other visual monitoring tools to guide daily decision-making by senior stakeholders and the company at large.
• Experiment with new kinds of visualizations that you believe could be better utilized in the organization.
• Re-work underlying code to appropriately structure visualization inputs.
• Communicate and present complex quantitative findings in easily digestible terms to company leadership, homing in on key takeaways.
• Concretely and informatively respond to any probing, on-the-spot follow-up questions from senior decision-makers.
• Conceive of new data assets and build automated transformations (via DBT, LookML, etc.) to bring them to fruition.
• Partner with Data Engineering teams to advance core data modeling/architecture (e.g. user clickstream logging), by optimizing, integrating, and distilling large raw datasets and metadata.
• Draw upon prior experience with expansive, unrefined datasets to fix modeling bottlenecks in quick, scalable, outside-of-the-box ways.

What you’ll bring

• 2+ years of experience in an analytics or