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Salary: USD 164,000 - 205,000 / annual
Boulevard is a client experience platform for appointment-based self-care businesses (salons, spas, medspas, barbershops, wellness). The company has processed over $5 billion in payments annually across 5,000+ customers and has raised $188M in funding.
You will build Boulevard's Product Intelligence function from the ground up, owning core responsibilities across three disciplines: data engineering (building and maintaining the tech stack), product analytics (analyzing feature use to support product decisions), and data science (experiment design, causal inference, behavioral modeling).
Key responsibilities include:
- Build the product data foundation by partnering across Product Development to define data capture requirements, design models, and create analysis-ready assets in partnership with data engineering.
- Develop data-driven recommendations that inform strategy through engaging narratives, storytelling, and visualizations for audiences from individual contributors to executives.
- Build scalable, self-serve dashboards that empower teams to explore data independently and make informed decisions. Operationalize product analytics and connect it to OKRs and business outcomes.
- Own deep-dive and exploratory analyses (funnel analysis, retention curves, cohort behavior, feature adoption) that surface insights proactively and support strategic business cases.
- Serve as the bridge between product data and the broader organization, ensuring insights inform cross-functional decisions.
- Create team processes and analytical workflows that enforce data accuracy and scale as the function grows.
- Own the experimentation practice for Product Development—design experiments before feature releases (hypothesis, metrics, randomization, power, duration), run analysis, and deliver clear reads on impact. Establish standards, templates, and Statsig workflows that make experimentation the default approach.
- Apply statistical and machine learning methods to explain and predict customer behavior: propensity and adoption models, retention and churn risk, segmentation, time-to-value modeling, and driver analysis. Choose the simplest method, validate rigorously, and communicate uncertainty clearly.
- Get model output into production workflows by partnering with data engineering to schedule, monitor, and version models. Land outputs where they drive action (in-product, Gainsight, Salesforce, CSM/PM workflows). Own model performance over time, including drift, retraining, and retirement.
This role is foundational work at an inflection point for Boulevard as the company scales upmarket and expands its product surface. You will define the questions, build the infrastructure to answer them, and chart the course forward—often without perfect information. You operate with a team-builder mindset, creating processes and documenting best practices that make the function scalable from day one.
REQUIREMENTS:
Required:
- 8+ years of proven experience in data science, product analytics, or engineering in a B2B SaaS or high-growth technology environment, with meaningful time in early-stage or low data-maturity environments (you've built the foundation, not just worked on top of one).
- Fluency with SQL, analytical tools (Python/Jupyter notebooks), Snowflake, DBT, Sigma, and AWS infrastructure for productionalizing analytics and models. Strong data modeling proficiency.
- Direct experience designing and executing product instrumentation strategies—defining event schemas, authoring tracking plans, ensuring reliable data capture in partnership with product and engineering.
- Expertise building dashboards and visualizations using Sigma, Looker, Tableau, or similar platforms, with a track record of creating self-serve tools teams actually use.
- Significant experience working directly with product managers and leaders, translating data findings into actionable opportunities and tradeoffs that drive strategy and roadmap investment.
- Demonstrated ability to design and execute deep-dive analyses across the full product lifecycle (funnel diagnostics, cohort and retention modeling, behavioral segmentation), translating statistical findings into clear, decision-ready narratives.
- Deep, hands-on statistical and machine learning expertise applied to customer behavior: regression and classification, propensity and churn-risk modeling, clustering and behavioral segmentation, survival and time-to-value analysis. Judgment to reach for the simplest method, validate honestly, and communicate uncertainty as clearly as the estimate.
- Proven experience owning experimentation end-to-end: designing tests before feature ships (hypothesis, primary and guardrail metrics, randomization unit, power, duration), running analysis, delivering defensible reads on impact. Hands-on experience with platforms like Statsig or Optimizely. Causal-inference toolkit and judgment to use it when clean A/B tests aren't possible.
- Ability to build and own your own data pipelines: production-grade DBT models, transformations, and orchestration in Snowflake, written in code with tests, documentation, and version control. Self-sufficient from raw event to analysis-ready asset; partner with data engineering on platform and scale rather than waiting in their queue.
- Clear, confident communication with stakeholders at any level—build narratives that land with PMs or executives, delivered with presence that builds trust.
- High level of ownership with demonstrated ability to manage projects end-to-end, identify opportunities, navigate ambiguity, and build scalable processes. Comfortable thriving in fast-paced, dynamic environments with multiple competing priorities.
- Proven track record of partnering cross-functionally and using product data to influence leadership decisions and outcomes (shaping go-to-market strategy, informing customer success priorities, driving alignment across teams). Earn trust by being direct, generous with knowledge, and consistent.
Preferred:
- Experience evaluating or implementing product analytics tooling such as Amplitude, Mixpanel, or similar platforms.
