Staff Data Scientist, Boulevard

• 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
• 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 estimate. Know Your Sh*t
• Proven experience owning experimentation end to end, designing tests before a feature ships (hypothesis, primary and guardrail metrics, randomization unit, power and duration), running the analysis, and delivering a defensible read on impact. Hands-on experience on a platform such as Statsig, Optimizely. Equally important: the causal-inference toolkit and the judgment to use it when a clean A/B test isn't possible. Know Your Sh*t
• 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. You're self-sufficient from raw event to analysis-ready asset, and you partner with data engineering on platform and scale rather than waiting in their queue. Act Like an Owner
• Clear, confident communication with stakeholders at any level — you can build a narrative that lands with a PM or the executive team, and you deliver it with the kind of presence that builds trust. Show Up With Style
• High level of ownership with a demonstrated ability to manage projects end-to-end, identify opportunities, navigate ambiguity, and build processes that scale — comfortable thriving in fast-paced, dynamic environments with multiple competing priorities. Make an Impact
• Proven track record of partnering cross-functionally and using product data to influence leadership decisions and outcomes — whether shaping go-to-market strategy, informing customer success priorities, or driving alignment across teams; you earn trust by being direct, generous with knowledge, and consistent in how you show up

Desirable

• We’re looking for a Staff Data Scientist to build Boulevard’s Product Intelligence function and own its core responsibilities
• 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
• 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