Staff Data Scientist

Who is Boulevard?

Boulevard provides the first and only client experience platform for appointment-based, self-care businesses. We empower our customers to give their clients more of the magical moments that matter most.

Before launching in 2016, our founders spent months interviewing salon managers and working behind front desks to understand their pain points so we could design a modern, user-friendly platform that meets the unique needs of their business. Our roots may be in hair salons, but we are built for the broader self-care industry, including many types of salons, spas, medspa, barbershops, and more. Our technology not only helps our customers survive but thrive. Take a look at how we (and YOU) can make that happen .

We have an insatiable curiosity and embrace experimentation. We believe that simple solutions require the most sophistication, and we design each and every detail to maximize potential, power, and impact. Do our values match? Read through our story and what we value the most .

Our team values and celebrates our diverse backgrounds. Being open about who we are and what we do allows us to do the best work of our lives. We believe in equal opportunity for all, and you should too.

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.