Manager, Product Data Analytics

Product Data Analytics Managers manage a team of highly skilled product data analyst that support our Product and Engineering teams, providing the final word on all analytics for CarGurus’ user and dealer experiences. The ideal candidate has a curiosity about seeing solutions in sprawling data sets and the business approach to convert insights into strategic opportunities for our company. You’ll work closely with leaders across engineering, product, data engineering, data science, marketing, and other go-to-market teams to support and implement high-quality, data-driven decisions. In doing so, you will gain exposure to multiple dimensions of the business and cross-functionally influence many different roadmaps in addition to your own.

• Exploration: Using your SQL/Python expertise, lead a team of analysts to conduct exploratory empirical analyses that bridge disparate data sources (e.g. clickstream data, subscription records, inventory volumes, etc.) to quantify product performance, user behavior, and/or market trends. Relentlessly dig into the data and collaborating with other individuals and teams.
• Process: Design and build technical processes to address business issues. Supervise the data/report requests process: tracking requests submitted, prioritization, approval, etc. Optimize processes for data intake, validation, mining and engineering as well as modeling, visualization and communicate outcomes.
• Metrics: Be responsible for the design and delivery of reports and insights that analyze business functions and key operations and performance metrics. Craft the metrics that define business success, condensing abstract or loosely-defined concepts down to concrete calculations. Audit and improve existing metrics to better advise the business’ needs.
• Advocacy: 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; thoughtfully and persuasively push back against potential missteps.
• Experimentation: Be a source of guidance for A/B experimentation, advising engineers, product managers, and high-level team members on 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. Suggest improvements to A/B testing-related tools, algorithms, and automated processes.
• Visualizations: Build intuitive dashboards and other visual monitoring tools to guide daily decision-making by senior partners and the company at large. Experiment with new kinds of visualizations that you believe could be better applied in the organization. Re-work underlying code to appropriately structure visualization inputs.
• Architecture: 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 logs), 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.
• Communication: 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.
• Training: Train, develop and supervise analyst-level employees. When required, be comfortable creating onboarding plans and training materials. Be proficient within our tech stack so that you can guide analysts towards innovative data solutions.

•  5+ years of demonstrated ability in an analytics or analytics-adjacent field, ideally involving sophisticated data modeling, quantitative analysis, and applied statistics.
• 2+ years experience managing a team.
• A natural thought leader with a consistent record of influencing business strategy through proven team member management skills.
• Expertise using SQL. Strong background in Python is highly desirable.
• Solid understanding of data mining principles: predictive analytics, mapping, user-tracking, collecting data from multiple data systems on premises and cloud-based data sources.
• Understanding of and experience using analytical concepts and statistical techniques: hypothesis development, designing tests/experiments, analyzing data, drawing conclusions, and developing practical recommendations for business units.
• Second-nature understanding of core statistical concepts (regression, significance testing, omitted variable bias, independence/dependence, etc.)
• Interest in creative data visualizations.
• Willingness to step outside your role and independently come up with novel ideas for the business.
• Able to challenge others’ ideas and advocate for your own.
• Excellent verbal and written communication skills. Very comfortable communicating high-impact and potentially critical findings to senior leadership.
• Strong project planning skills, with experience building roadmaps, estimating required resources, and flagging inter-dependencies with other teams/projects.
• Strong ability to plan and prioritize numerous processes, people and projects simultaneously.

The exceptional candidate will also be proficient and experienced with the following tools/programs:

• Looker
• Snowflake
• DBT
• Salesforce
• Jira
• Snowplow
• Google Suite
• Microsoft Suite