Senior Data Scientist

November 6, 2026

Remote (within country)

Senior

United States

Description

Senior Data Scientist, Product Growth and Experimentation, focused on driving decisions in support of the Product Led Growth function. The role sits at the intersection of data, product, growth, and GTM, illuminating the user journey from initial interest to full adoption. The scientist designs and evaluates experiments, builds analytical frameworks, reusable datasets and dashboards, and communicates findings to technical and non-technical audiences.

Responsibilities

Act as an embedded Data partner to Product, Design, and Engineering, helping shape strategy, product proposals, and experiments from the earliest stages
Build analytical frameworks across the full PLG journey from acquisition and activation through engagement, adoption, expansion, monetization, and durability
Use funnel, cohort, journey, and sequence analysis to identify friction and opportunities for improvement
Identify the behaviors and milestones that predict conversion, production readiness, expansion, churn, and long-term customer success, and translate them into leading indicators
Analyze feature discovery and adoption patterns to recommend interventions that increase successful adoption and expansion
Design and evaluate experiments and product changes using matched comparisons, pre/post-intervention analysis, and causal methods when conventional A/B testing is not practical
Clearly distinguish observed results from assumptions, quantify uncertainty, and explain the confidence behind each conclusion
Help prioritize growth opportunities based on potential impact, confidence, effort, and measurement feasibility
Build reusable datasets, metrics, dashboards, and analytical tools that reduce ad hoc work and make insights accessible
Partner with Engineering and Data Engineering to improve event instrumentation, data quality, and the analytical models required for reliable product analysis
Communicate findings through clear visualizations, written narratives, and practical recommendations for technical and non-technical audiences
Audit signals currently captured in the onboarding flow and recommend new instrumentation as needed
Establish reporting and repeatable analysis for PLG experiments
Analyze user behavior after the first successful Activity or Workflow
Identify signals of production readiness and common points where users stall
Map feature-adoption paths across important product areas

Requirements

Strong foundation in statistics, experimental design, causal reasoning, and quantitative analysis
Experience using product, behavioral, or event data to improve activation, engagement, retention, conversion, or expansion
Experience working with small samples, noisy signals, imperfect control groups where standard A/B testing is not always possible
Strong judgment in choosing methods that fit the decision, available data, and level of uncertainty
Proficiency in SQL and Python for analysis, modeling, and automation
Experience with cohort analysis, funnel analysis, segmentation, predictive modeling, and supervised or unsupervised machine-learning methods
Ability to prototype quickly in notebooks and convert recurring analyses into reliable data products, models, or workflows
Experience with modern data platforms and query engines such as Athena, Presto/Trino, BigQuery, or Snowflake
Familiarity with cloud and object-store technologies such as AWS S3
Experience building dashboards and reusable analytical assets in a modern business intelligence platform
Comfort working with evolving definitions, incomplete instrumentation, and trade-offs between analytical precision and decision usefulness
Results-oriented mindset and a record of translating analysis into product or business action
Strong communication skills including the ability to explain methods, uncertainty, and recommendations in plain language
Curiosity about developer platforms, cloud infrastructure, usage-based products, and how customers adopt technically complex products
Collaborative approach and the ability to work effectively with Product, Engineering, Marketing, Sales, Finance, and Data partners