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.

Boulevard is the client experience platform purpose-built for salons, spas, medspas, and wellness businesses. More than 5,000 businesses use Boulevard to manage scheduling, payments, marketing, and client relationships — processing over $5 billion in payments annually. We’ve raised $188M in funding and are growing fast, particularly as we expand upmarket into multi-location and franchise operators.

We’re looking for a Staff Data Scientist to build Boulevard’s Product Intelligence function and own its core responsibilities. You have deep and broad experience in as many of these three disciplines 1. Doing the engineering work to build and maintain the tech stack 2. Data analysis and insights on product feature use to support product decisions 3. Using data science approaches like experiment design, measuring experiment results and building models that helps us understand customer behavior and customer product use better. You know what good looks like, you hold yourself and your work to that standard, and you don’t wait to be asked before surfacing what matters.

This role requires someone who is genuinely energized by ambiguity. There is no well-worn path to follow, you’ll be defining the questions, building the infrastructure to answer them, and charting the course forward — often without perfect information. That’s not a warning; for the right person, it’s the whole appeal.

You operate with the mindset of a team builder — creating processes, documenting best practices, and working with the structure and rigor that makes this function scalable from day one.

Key Responsibilities

Build Boulevard’s product data foundation - partnering across the Product Development organization to define what needs to be captured and how, and designing the models and the tech stack that translate raw data into clean, reliable and scalable analysis-ready assets in partnership with data engineering

In tight partnership with Product, develop data-driven recommendations that inform strategy and drive action — through engaging narratives, effective data storytelling, and visualizations adapted to the audience, from individual contributors to executive leadership

Build scalable, intuitive and self-serve dashboards that empower teams and stakeholders to independently explore data and make informed strategic decisions; fostering a data-driven culture by educating and enabling stakeholders to interpret data and act on it with confidence. Operationalize product analytics. Connect product analytics to OKRs and business outcomes.

Own deep-dive and exploratory analyses that up-level understanding of customers and their relationship with the product (e.g. funnel analysis, retention curves, cohort behavior, feature adoption) — surface insights proactively and build analytical narratives that support strategic business cases and influence product direction

Be the bridge between product data and the broader organization — ensuring insights actively inform and influence cross-functional decisions and outcomes

Create team processes and analytical workflows that enforce data accuracy and scale as the function grows; advocate for the tooling investments the team requires

Experimentation, design, not just readout. Own the experimentation practice for Product Development — partner with PMs and engineering to design experiments before feature releases (hypothesis, primary and guardrail metrics, unit of randomization, power and duration), then run the analysis and deliver a clear, defensible read on impact. Establish the standards, templates, and Statsig/tooling workflows that make experimentation the default way Boulevard evaluates a feature launch, and be honest about when a clean test isn't possible — designing the best available quasi-experimental read (pilot cohorts, staged rollouts, difference-in-differences, pre/post with controls) instead.

Modeling customer behavior. Apply statistical and machine learning methods to explain and predict customer behavior — propensity and adoption models, retention and churn risk, segmentation and clustering of usage patterns, time-to-value and activation modeling, and driver analysis that separates correlation from cause. Choose the simplest method that answers the question, validate rigorously (holdouts, backtesting, recall/precision trade-offs framed by business cost), and communicate uncertainty as clearly as the point estimate.

Get model output into the workflow. Take models from analysis to production — partner with data engineering to schedule, monitor, and version them, and land the output where it drives action (in-product surfaces, Gainsight, Salesforce, CSM and PM workflows). Own model performance over time, including drift, retraining, and retiring models that stop earning their keep.

What You’ll Need to Thrive

Required

8+ years of proven experience in data science or product analytics or engineering in a B2B SaaS or high-growth technology environment, with meaningful time spent in early-stage or low data-maturity environments — you’ve built the foundation, not just worked on top of one someone else laid. Act Like an Owner

Fluency with data analysis and BI tools: SQL, analytical tools like Python / Jupyter notebooks, Snowflake, DBT, Sigma for data and reporting pipelines and AWS infrastructure to productionalize analytics / models in a repeatable way; strong proficiency with data modeling. Know Your Sh*t

Direct experience designing and executing product instrumentation strategies — defining event schemas, authoring tracking plans, and ensuring reliable data capture in partnership with product and engineering

Expertise in building dashboards and visualizations using platforms such as Sigma, Looker, Tableau, or similar — with a track record of creating self-serve tools that 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 — including funnel diagnostics, cohort and retention modeling, and behavioral segmentation — translating statistical findings into clear, decision-ready narratives for product and leadership audiences

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 — with the judgment to reach for the simplest method that answers the question, validate it honestly, and communicate uncertainty as clearly as the es