Staff Data Scientist

Come do the best work of your life at Boulevard.

We’re looking for a Staff Data Scientist to build Boulevard’s Product Intelligence function and own its core responsibilities. You’ll define the questions, build the infrastructure to answer them, and chart the course forward, often without perfect information. For the right person, that ambiguity is the whole appeal. You’ll work with a team-builder’s mindset, creating processes and documenting best practices so the function scales from day one.

Boulevard is at an inflection point: we’re scaling upmarket, expanding our product surface, and making bigger bets on where the business goes next. Every one of those bets needs a trusted picture of what’s happening in the product today. This is foundational work that compounds. What you build in your first year will shape how we make product decisions for the next five, and your impact will be visible every day.

What you’ll do here:

• Build Boulevard’s product data foundation: define what we capture and how, and design the models and tech stack that turn raw data into clean, reliable, analysis-ready assets, in partnership with data engineering.
• Partner tightly with Product to turn data into recommendations that drive action, using clear narratives and visualizations for everyone from individual contributors to executives.
• Build self-serve dashboards teams actually use, connect product analytics to OKRs and business outcomes, and help stakeholders read data and act on it with confidence.
• Own deep-dive analyses like funnels, retention curves, cohort behavior, and feature adoption, and surface insights proactively to influence product direction.
• Create team processes and analytical workflows that keep data accurate as the function grows, and advocate for the tooling the team needs.
• Own the experimentation practice for Product Development: design tests before launch, deliver a defensible read on impact, set the standards and templates that make experimentation the default, and design the best quasi-experimental read when a clean test isn’t possible.
• Apply statistical and machine learning methods to explain and predict customer behavior, such as adoption, churn risk, and segmentation, choosing the simplest method that answers the question.
• Take models from analysis to production with data engineering, landing output where it drives action (in-product surfaces, Gainsight, Salesforce, CSM and PM workflows), and own model performance over time.

What you’ll need to thrive:

• 8+ years in data science, product analytics, or engineering in a B2B SaaS or high-growth tech environment, including meaningful time in early-stage or low data-maturity settings where you built the foundation yourself.
• Fluency with SQL, Python and Jupyter, Snowflake, dbt, Sigma, and AWS, plus strong data modeling. You write production-grade dbt models with tests, documentation, and version control, and you’re self-sufficient from raw event to analysis-ready asset while partnering with data engineering on scale.
• Direct experience designing product instrumentation: event schemas, tracking plans, and reliable data capture with product and engineering.
• Expertise building dashboards and visualizations in Sigma, Looker, Tableau, or similar, with a track record of self-serve tools that teams actually use.
• Deep-dive analysis experience across the product lifecycle, including funnel diagnostics, cohort and retention modeling, and behavioral segmentation, turned into decision-ready narratives for product managers and leadership.
• Hands-on statistical and machine learning expertise applied to customer behavior: regression and classification, propensity and churn-risk modeling, clustering, and survival or time-to-value analysis, with the judgment to validate honestly and communicate uncertainty as clearly as the estimate.
• Experience owning experimentation end to end, from designing tests (hypothesis, metrics, randomization unit, power, duration) to a defensible read on impact, on a platform like Statsig or Optimizely, plus the causal-inference toolkit for when a clean A/B test isn’t possible.
• Clear, confident communication with stakeholders at any level, strong ownership of projects end to end, and a track record of influencing leadership decisions across product, go-to-market, and customer success, while being direct, generous with knowledge, and consistent.
• A plus: experience evaluating or implementing product analytics tooling such as Amplitude or Mixpanel.

How we’ll take care of you:

At Boulevard, we work hard to structure compensation in a way that balances internal equity with local market competitiveness, and we're happy to share a good-faith estimate of the base salary range for this role. For U.S. based candidates, the anticipated base salary range is $171,400 - $201,800 per year. In addition to this base compensation, this role may be eligible to participate in a variable compensation program. Final compensation will vary based on a variety of factors which include but are not limited to applicable experience, location, and final leveling.