About the Role

We're looking for a curious, technically strong data professional to join our Corporate Ventures and AI Operations team. This is a hybrid role sitting at the intersection of data engineering, data science, and revenue operations — you'll build and maintain the pipelines that power our ads data, develop recommendation algorithms that keep users coming back, own commission and quota-setting processes for our ad sales team, and work closely with business stakeholders to translate data into decisions.
You care about the milliseconds between a user opening the app and finding something they love. You think about what makes someone stay, subscribe, and come back tomorrow — and you build the systems that make that happen at scale.

Responsibilities

• Instrument and monitor performance signals (load times, stream quality, error rates) to ensure users experience fast, flawless products across web, app, and live streams
• Build and maintain dashboards that track engagement and retention metrics — session length, scroll depth, return rate, and drop-off points
• Identify friction in the user journey through data and partner with product and engineering to eliminate it
• Design and analyze experiments that test retention-boosting product changes

• Build and iterate on recommendation algorithms that surface the right content to the right user at the right moment
• Develop the data infrastructure (feature pipelines, training datasets, feedback loops) that keeps recommendation models fresh and accurate
• Measure the impact of personalization on session time, click-through rates, and long-term retention through rigorous experimentation
• Partner with the content and product teams to translate algorithmic output into intuitive user experiences

• Build, maintain, and improve data pipelines that process large-scale ads, user event, and engagement data
• Write clean, well-tested SQL and Python/PySpark code to transform raw data into reliable, analysis-ready datasets
• Oversee the data architecture supporting ad-tech integrations, paywall flows, and subscription checkout — ensuring these monetization surfaces are instrumented, reliable, and fast
• Monitor data quality and proactively surface and resolve data integrity issues
• Partner with platform and engineering teams to ensure pipelines are scalable and well-documented

• Design and analyze A/B experiments to measure the impact of ads, growth, and personalization initiatives
• Build predictive models (e.g., propensity, churn, re-engagement) to improve targeting and campaign performance
• Develop dashboards and reports that give the business clear visibility into key metrics
• Translate ambiguous business questions into structured analytical frameworks

• Own the end-to-end commission calculation process for the ad sales team — ensuring accuracy, timeliness, and clear documentation
• Partner with Sales and Finance to design and model sales quotas, incorporating historical performance data, market benchmarks, and business targets
• Build and maintain models to simulate commission payout scenarios and evaluate the cost and incentive impact of quota and comp plan changes
• Work with cross-functional partners (product, marketing, sales) to understand their data needs and deliver actionable insights
• Proactively identify trends and anomalies in ads performance and communicate them clearly to stakeholders
• Support operational reporting and help automate recurring analyses to free up team bandwidth

Requirements

• Bachelor's or Master's degree in a quantitative field (Statistics, Computer Science, Economics, Mathematics, Engineering, or similar)
• Strong SQL skills; comfort writing complex queries across large datasets
• Experience with Python for data analysis (pandas, numpy, scikit-learn, etc.)
• Familiarity with distributed data processing tools (Spark, Hive, or similar)
• Understanding of A/B testing and experimental design principles
• Ability to communicate data findings clearly to both technical and non-technical audiences

Preferred Qualifications

• Exposure to ads data, growth metrics, or attribution frameworks
• Experience with recommendation systems, collaborative filtering, or content ranking
• Familiarity with ad-tech concepts: ad serving, DSPs, SSPs, header bidding, or paywall/subscription systems
• Familiarity with cloud data platforms (Databricks, BigQuery, Snowflake, or similar)
• Experience building ML models in a production or near-production context
• Prior internship or work experience at a tech, media, or consumer company

Benefits

We offer a competitive benefits package:

• Health, dental, and vision care for you and your family (100% coverage for employee)
• Top-tier 401(K) plan with company matching
• Paid time off and paid holidays
• FSA, HSA and commuter benefits programs
• Team activity budget