Data Scientist, Replit

• Bachelor's degree in Computer Science, Statistics, Mathematics, Economics, or related field, OR equivalent real-world experience in data roles
• 2-4 years of experience in data science, analytics, or related roles with a focus on marketing, growth, or business analytics
• Strong SQL skills and experience working with large datasets, particularly event-level user behavior data, and designing ETL workflows using dbt
• Proficiency in Python and data science libraries (pandas, scikit-learn, statsmodels, etc.)
• Experience designing and analyzing A/B tests and experiments, including statistical rigor around sample sizing, significance testing, and causal inference
• Experience building dashboards and visualizations (Looker, Tableau, Mode, or similar tools)
• Ability to translate ambiguous business questions into structured analysis and communicate findings clearly to non-technical stakeholders

Desirable

• Our mission is to achieve Autonomy for All: making programming accessible, collaborative, and powered by AI
• To realize this vision, we are building a brand that is as iconic, inventive, and human as the product itself
• You'll directly impact Replit's growth by turning user behavior into actionable insights that optimize our marketing efforts, improve conversion funnels, and drive sustainable revenue growth across our self-serve and enterprise segments
• Design and analyze marketing experiments to optimize campaigns, messaging, and channel performance across email, paid ads, social, and content marketing
• Build attribution models and multi-touch conversion funnels to understand the customer journey from first touch to paid conversion
• Develop predictive models to identify high-intent prospects, optimize lead scoring, and improve targeting for paid acquisition campaigns
• Partner with marketing, growth, and revenue teams to translate business questions into rigorous analysis and clear recommendations
• Create self-service dashboards and automated reporting that surface key marketing metrics (CAC, LTV, ROAS, conversion rates) for go-to-market teams
• Build and maintain data pipelines that integrate marketing platforms (Google Ads, Meta, Iterable, Segment, etc.) with our product analytics
• Examples of what you could do:
• Build propensity models to identify which free users are most likely to convert to plans based on usage patterns and engagement signals
• Analyze cohort behavior and retention patterns to optimize lifecycle marketing campaigns and reduce churn
• Develop segmentation models to personalize messaging and targeting for different user personas (students, hobbyists, professional developers, enterprise teams)
• Build real-time alerting systems to flag anomalies in campaign performance or conversion metrics, automate bidding adjustments across platforms