Staff Data Scientist

Accountabilities:: Build the product data foundation by defining instrumentation requirements, event schemas, data models, and scalable analysis-ready assets in partnership with data engineering. Partner closely with Product teams to translate data into actionable recommendations through clear narratives, visualizations, and audience-appropriate data storytelling. Build scalable, intuitive, self-service dashboards that enable teams and leaders to independently explore product data and connect product analytics to OKRs and business outcomes. Lead deep-dive and exploratory analyses covering funnels, retention, cohorts, feature adoption, customer behavior, and usage patterns to identify opportunities and inform product direction. Serve as a bridge between product data and the broader organization, ensuring insights influence cross-functional decisions and strategic outcomes. Establish analytical processes, workflows, documentation, and quality standards that improve data accuracy and allow the function to scale effectively. Own the product experimentation practice, partnering with Product Managers and Engineering to define hypotheses, metrics, randomization approaches, test duration, power requirements, and analysis plans before launches. Establish experimentation standards, templates, and tooling workflows while applying appropriate quasi-experimental methods when traditional A/B testing is not feasible. Apply statistical and machine learning techniques to understand and predict customer behavior, including propensity, adoption, churn, retention, segmentation, time-to-value, and driver analysis. Productionize models in partnership with data engineering, ensuring outputs reach operational workflows such as product experiences, customer success platforms, CRM systems, and Product or Customer Success processes. Monitor model performance over time, including drift, retraining, validation, and retirement of models that no longer provide sufficient business value. Advocate for appropriate analytics and data tooling investments while continuously improving the team's ability to work efficiently and independently. Requirements: 8+ years of experience in data science, product analytics, data engineering, or a related discipline within a B2B SaaS or high-growth technology environment. Proven experience working in early-stage or low-data-maturity environments where you have built data foundations and scalable analytical capabilities rather than relying solely on established infrastructure. Strong proficiency with SQL, Python, Jupyter notebooks, Snowflake, dbt, Sigma, AWS, and modern data modeling and analytics practices. Hands-on experience building production-grade dbt models, transformations, pipelines, testing, documentation, orchestration, and version-controlled workflows. Direct experience designing product instrumentation strategies, including event schemas, tracking plans, and reliable data capture in partnership with Product and Engineering. Significant experience developing self-service dashboards and visualizations using Sigma, Looker, Tableau, or similar platforms. Strong background partnering with Product Managers and senior leaders to translate analytical findings into strategic recommendations, trade-offs, and roadmap decisions. Demonstrated expertise in product analytics, including funnel analysis, cohort and retention analysis, behavioral segmentation, feature adoption, and customer lifecycle analysis. Deep statistical and machine learning expertise, including regression, classification, propensity and churn modeling, clustering, survival analysis, time-to-value analysis, and causal inference. Proven experience owning experimentation end to end, including hypothesis development, primary and guardrail metrics, randomization, power and duration, analysis, and communication of results. Hands-on experience with experimentation platforms such as Statsig or Optimizely, as well as alternative causal-inference approaches when controlled experiments are not possible. Strong ability to select appropriate analytical methods, validate models rigorously, assess uncertainty, and communicate limitations clearly. Excellent communication and data storytelling skills, with the ability to build trust and present compelling insights to both technical stakeholders and executive leadership. High level of ownership, initiative, and adaptability, with demonstrated ability to manage projects end to end, navigate ambiguity, identify opportunities, and establish scalable processes. Strong cross-functional collaboration skills and a track record of using product data to influence leadership decisions and business outcomes. Experience with product analytics platforms such as Amplitude or Mixpanel is preferred. Benefits: Base salary of $164,000–$205,000 for candidates in NYC, the San Francisco Bay Area, and Seattle. Base salary of $171,400–$201,800 for candidates in other U.S. locations. Eligibility for a variable compensation or performance bonus program. Equity participation for full-time employees. Comprehensive medical coverage with up to 100% employee premium coverage and 75% dependent coverage. $1,200 annual employer HSA contribution for eligible plans. 100% covered dental, vision, life, and disability insurance. 12 weeks of fully paid U.S. parental leave for all new parents. Flexible paid time off and 10 U.S. company holidays. Fully remote work arrangement. $200 home office setup allowance and $50 monthly work-from-home reimbursement. $200 quarterly wellness and lifestyle allowance. One Medical membership and Employee Assistance Program. Supportive culture focused on sustainable performance, flexibility, and employee well-being.