As a Data Analyst for Product at CircleCI, you will:
- Lead all things data for a team in our Product organization
- Deeply understand what drives our customers and products
- Analyze large, rich data sets
- Improve our product to help 100,000+ developers build and deliver software faster
About Product’s Data Science and Analytics team at CircleCI
You will be a member of CircleCI’s 8-person data science and analytics team reporting to our director. The data science and analytics team is focused on improving our product experience and building a data-driven product culture.
You will be embedded into a CircleCI product team. Your team will be led by a Product Manager and will span Product, Design, and Engineering. The team is chartered to deliver measurable improvements to specific CircleCI products and services. You will be the product team’s lead for all things data.
What you’ll do:
- Be a brilliant analyst: Turn business problems into research plans. Leverage data from our data lake, data warehouse, source systems, and third parties. Work confidently with clean and messy data. Assess, clean, and explore our data. Soundly use statistics and probability. Tell compelling stories. Build impactful visualizations. Present insights to peers and executives. Drive action and outcomes. Turn repetitive analyses into dashboards.
- Support a product team: Partner with product managers. Build context and ask smart questions. Brainstorm ideas, experiments, and hypotheses. Build data-driven business cases. Help set team goals. Define, document, monitor, and forecast key metrics. Perform root cause analyses on key metrics. Define requirements for developers to improve data instrumentation. Build data pipelines and tables.
- Understand our product and customers: Analyze product features, journeys, and life cycles. Drive conversion, engagement, and efficiency across our product funnel. Improve activation, monetization, engagement, and retention. Identify important customer characteristics and behaviors. Improve segmenting and cohorting.
- Leverage a growth toolkit: Define the models, paths, and loops of growth for your product. Identify and prioritize opportunities for incremental improvements. Work in cycles of build, measure, and learn. Define, implement, and interpret A/B tests and other experiments. Educate stakeholders on statistical validity.
- Improve our analytics practices: Scale success. Celebrate failing fast. Mature from one-off tactics to repeatable processes. Embrace not just quantitative insights but also qualitative; not just analytics, but also creative thinking. Define requirements for data engineering to improve data transformation, automation, and analytics capabilities.
What we’re looking for:
- 3 years of analytics experience: At least 3 years of analytics experience. Prior experience in an analytics role specifically focused on product or growth is highly preferred
- Experience at a high-growth company: Analytics experience with high-growth businesses. Ideally, those with well-defined, product-enabled funnels and complex, high-volume customer flows.
- Technical education: A bachelor’s degree in a technical or quantitative field (e.g., Analytics, Computer Science, Data Science, Economics, Engineering, Finance, Mathematics, or Statistics,). A similar Master’s degree is a bonus.
- Proficiency across analytics tools: Deep experience with analytical SQL queries (e.g., inner/left/right joins, CTEs, and window functions). Experience scripting in a notebook environment, preferably using Python, Pandas, and Jupyter. A desire to grow your modeling, machine learning, and data science skills. Familiarity with basic statistics (e.g., descriptive statistics, experiments, significance testing, and linear regression). Experience with a major analytics/visualization platform (e.g., Tableau or Looker).
- Proficiency across growth tools: Productive with growth tools. Specifically a product or events analytics platform (e.g., Google Analytics, Amplitude, or Segment) and an A/B testing tool (e.g., Optimizely, VWO, or a home-grown implementation).
- Right soft skills: Passion for data, discovery, and problem-solving. Curious; confident; open to questioning assumptions and being questioned. Effective communicator across technical and business audiences. Able to manage stakeholder expectations and work with hands-on executives. Entrepreneurial; fast-moving; able to balance vision and execution; and not easily discouraged. An instinct for value and focused on the incremental.