Data Scientist 1447 views

Antenna is building next generation measurement & analytics solutions for subscription services like Netflix, Hulu, Spotify, and beyond.

Antenna will be the de facto measurement standard for the subscription economy, focusing companies on success metrics that matter so subscription companies can build healthy, sustainable businesses rather than chasing vanity metrics and short-term goals.

They’re looking for a Data Scientist to help bring v1 of their product to market – and beyond.

In this role, you’ll get to choose which metrics should define success and how they should measure them. If you’re obsessed with how data informs product & marketing, how unit economics drive subscription businesses, and how measurement models influence content, this is the role for you.

Who You Are

• You have proven the ability to take a product from concept to launch, working cross-functionally with engineering, analytics, and revenue to create something that matters.
• You’re craving the intense, rewarding experience of building a tool that positively influences everything we watch, buy, and listen to from the ground up.
• You love data – and enjoy pushing the boundaries of what insights can be extracted out of data.
• Proficient in Python.
• Proficient in PostgreSQL.
• Experience with advanced data modeling and predictive analytics.
• Deep understanding of statistics concepts (e.g., probability distribution, regression analysis).
• Can explain how models and systems work to both non-technical and technical stakeholders.
• ~3+ years of experience accessing databases and building models to analyze user behavior.
• English proficiency.

What You’ll Do

• Define success metrics such as churn rate and retention rate and determine the best way to calculate them.
• Deep dive analysis into user cohort behavior: how do different user cohorts behave?
• Predictive modeling to predict trends (e.g. cancellation behavior) in those user cohorts.
• Partner closely with Data Analysts to provide our clients with insightful, actionable analysis.
• Conduct industry-specific analyses such as customer lifetime value analysis, winback analysis, and active vs. passive churn analysis.
• Use statistical inference to project our data onto the general population.