The ad says“Experience designing and analyzing A/B tests and experiments, including rigor around sample sizing, power analysis, significance testing, novelty effects, interference between experiments, and causal inference”
The ad says“You leverage AI tools extensively in your own analytical workflow and can demonstrate how they make you more effective, while maintaining high standards for output quality”
The ad says“Experience designing and analyzing A/B tests and experiments, including rigor around sample sizing, power analysis, significance testing, novelty effects, interference between experiments, and causal inference”
The ad says“Strong SQL skills and experience working with large datasets, particularly event-level user behavior data, and designing ETL workflows using dbt”
The ad says“Strong SQL skills and experience working with large datasets, particularly event-level user behavior data, and designing ETL workflows using dbt”
The ad says“Proficiency in Python and data science libraries (pandas, scikit-learn, statsmodels, etc.)”
The ad says“Experience designing and analyzing A/B tests and experiments, including rigor around sample sizing, power analysis, significance testing, novelty effects, interference between experiments, and causal inference”
The ad says“Proficiency in Python and data science libraries (pandas, scikit-learn, statsmodels, etc.)”
The ad says“Proficiency in Python and data science libraries (pandas, scikit-learn, statsmodels, etc.)”
The ad says“Experience designing and analyzing A/B tests and experiments, including rigor around sample sizing, power analysis, significance testing, novelty effects, interference between experiments, and causal inference”
The ad says“Strong SQL skills and experience working with large datasets, particularly event-level user behavior data, and designing ETL workflows using dbt”
The ad says“Proficiency in Python and data science libraries (pandas, scikit-learn, statsmodels, etc.)”
Values
The ad says“Experience with modern data stack (dbt, BigQuery, Snowflake, Fivetran, etc.) and product analytics platforms (Amplitude, Mixpanel, Segment, etc.)”
The ad says“Experience with modern data stack (dbt, BigQuery, Snowflake, Fivetran, etc.) and product analytics platforms (Amplitude, Mixpanel, Segment, etc.)”
The ad says“Experience designing ETL workflows and data pipelines using dbt or similar tools”
The ad says“Experience with causal inference methods (difference-in-differences, synthetic control, propensity score matching)”
The ad says“Experience with modern data stack (dbt, BigQuery, Snowflake, Fivetran, etc.) and product analytics platforms (Amplitude, Mixpanel, Segment, etc.)”
The ad says“Experience with modern data stack (dbt, BigQuery, Snowflake, Fivetran, etc.) and product analytics platforms (Amplitude, Mixpanel, Segment, etc.)”
The ad says“Experience with causal inference methods (difference-in-differences, synthetic control, propensity score matching)”
The ad says“Experience with modern data stack (dbt, BigQuery, Snowflake, Fivetran, etc.) and product analytics platforms (Amplitude, Mixpanel, Segment, etc.)”
The ad says“Experience with modern data stack (dbt, BigQuery, Snowflake, Fivetran, etc.) and product analytics platforms (Amplitude, Mixpanel, Segment, etc.)”
The ad says“Experience with causal inference methods (difference-in-differences, synthetic control, propensity score matching)”
Design and analyze product experiments to evaluate feature launches, onboarding changes, and in-product interventions with rigorous statistical methodology
Own the analytics for core product areas — Growth (activation, engagement, monetization & retention), feature adoption, product quality, AI agent effectiveness, and proactively surface insights that influence product roadmap decisions
Build the analytical foundation for Replit’s enterprise business, understanding team adoption patterns, workspace collaboration dynamics, and expansion signals that inform go-to-market and product strategy
Develop predictive models to forecast frameworks to measure impact of features and new launches on user behavior, including likelihood to retain, convert, or expand, and embed those signals directly into product and growth workflows
Examples of what you could do:
Design and analyze a complex multi-variant experiment testing pricing, onboarding, and feature gating simultaneously, navigating interaction effects and providing clear recommendations despite ambiguity
Model the “aha moment” for new Replit users — identifying early behaviors most predictive of long-term retention — and work with the product team to redesign onboarding around those signals
Analyze feature adoption and cohort behavior across user segments (students, hobbyists, professional developers, enterprise teams) to help PMs prioritize the roadmap with confidence
Analyze enterprise team adoption patterns to identify what drives successful rollouts versus stalled deployments, and build the data models that power Replit’s enterprise expansion playbook
Strong SQL skills and experience working with large datasets, particularly event-level user behavior data, and designing ETL workflows using dbt
Bachelor’s degree in Computer Science, Statistics, Mathematics, Economics, or related field, OR equivalent real-world experience in data roles
Proficiency in Python and data science libraries (pandas, scikit-learn, statsmodels, etc.)
Experience designing and analyzing A/B tests and experiments, including rigor around sample sizing, power analysis, significance testing, novelty effects, interference between experiments, and causal inference
5+ years of experience in data science with a focus on product analytics, growth, or user behavior
You leverage AI tools extensively in your own analytical workflow and can demonstrate how they make you more effective, while maintaining high standards for output quality
You’re a data scientist who moves fast and goes deep. You can spin up an analysis in hours that would take others days; not by cutting corners, but because you’ve built the intuition and technical toolkit to get to the right answer quickly
You’re the person who digs past the top-line number to find the confound, questions whether the metric actually measures what people think it does, and pressure-tests your own work before anyone else sees it. You treat experimentation as a craft, not a checkbox. You’ve felt the pain of underpowered tests and novelty effects, and you have the judgment to get it right
You use AI agents and tools aggressively to multiply your output - writing code, exploring data, generating hypotheses, but you treat every AI-assisted output as a draft, not a deliverable
You know what good analysis looks like and you won’t ship anything that doesn’t meet that bar. The result is that you operate at a speed and depth that most DS teams can’t match
Understanding of developer tools, collaborative coding environments, or technical products
You’ve built or contributed to AI-powered analytical tools, automation, or novel measurement approaches
Experience working directly embedded with product teams in an agile environment
Experience with modern data stack (dbt, BigQuery, Snowflake, Fivetran, etc.) and product analytics platforms (Amplitude, Mixpanel, Segment, etc.)
Experience designing ETL workflows and data pipelines using dbt or similar tools
Experience analyzing freemium or usage-based pricing models
Familiarity with customer data platforms (CDPs) and event tracking implementation
