Data Analyst

Who We Are (Mode.Inc)

Mode is a fast-growing technology company building businesses at the intersection of AI, data, mobile apps, and digital experiences. Our portfolio of mobile apps and digital products reaches 100M+ monthly active users across a global audience. We continue to accelerate growth through product innovation, strategic acquisitions, and emerging business opportunities.

Mode was recognized by Deloitte as the #1 fastest-growing software company in North America on the 2023 Technology Fast 500. Today, we are building our next phase of growth, including expanding Mode's AI and data business and developing direct relationships with companies that can benefit from the data and capabilities we have built.

Our Team and Culture Drive Our Success

Our team is the heart of our culture.  We value innovation, collaboration, and curiosity, with a passion for getting things done.  We strive to hire and cultivate the best team members and foster a culture of open communication and transparency.  We are driven and hard-working but also committed to having fun along the way.  We allow OKRs to guide us, assume positive intent, sometimes agree to disagree and commit, honor our commitments, seek to accomplish more with less, and encourage everyone to speak up as our team owns our company’s success.

You can read more about our team and how we work here.

About the Role

We’re looking for a Senior Data Analyst to be the analytical partner to our User Acquisition team. You’ll own the measurement of how we acquire users across our portfolio of mobile apps and web properties — from attribution and channel performance to cohort LTV and payback — and turn that into clear recommendations on where to spend the next dollar. 

The ideal candidate has exceptional analytical skills, strong SQL and Python skills, deep familiarity with mobile app marketing data, and can bring rigor to data quality, analysis and storytelling.

What You’ll Do

• Design, build, and maintain common datasets and data pipelines that strengthen self-service analytics and enable data-driven decision making across the organization
• Improve data quality, accuracy, reliability, and documentation to strengthen self-service analytics
• Own UA performance measurement across paid channels (e.g., Meta, Google, TikTok, AppLovin, Unity), including CPI/CAC, ROAS, retention, and payback by channel, campaign, geo, and creative
• Build and maintain predictive LTV models and cohort curves that let the UA team make bid and budget decisions on early signals