Data Scientist – Analytics

About the role

About AppLovin

AppLovin makes technologies that help businesses of every size connect to their ideal customers. The company provides end-to-end advertising solutions for businesses to reach, monetize and grow their global audiences. For more information about AppLovin, visit: www.applovin.com.

To deliver on this mission, our global team is composed of team members with life experiences, backgrounds, and perspectives that mirror our developers and customers around the world. At AppLovin, we are intentional about the team and culture we are building, seeking candidates who are outstanding in their own right and also demonstrate their support of others.

We are seeking a Data Scientist – Analytics to join our team and help bridge the gap between engineering and business functions at AppLovin. In this role, you will work with petabyte-scale datasets across our ad and app ecosystem, analyzing high-velocity data streams generated by tens of millions of daily active users. You will support the development of analytics tools, monitoring systems, and reporting pipelines that improve visibility into product health, business performance, and model outcomes. You’ll partner closely with research scientists, engineers, and business stakeholders to uncover insights, diagnose issues, and identify opportunities for growth.

This role is ideal for someone early in their data science career who enjoys solving ambiguous problems, has foundational statistical and modeling knowledge, and is excited to build impactful tools used across the organization.

Key Responsibilities

Analytics, Monitoring & Tooling

- Build and maintain dashboards, monitoring systems, and automated reporting to track product, business, and model performance.

- Develop scalable analytics pipelines to surface key metrics, detect anomalies, and support timely issue diagnosis..

- Define and refine KPIs, using structured, hypothesis-driven analysis to understand performance changes and long-term trends.

Insights Discovery & Model Evaluation

- Analyze large datasets to identify trends, diagnose performance changes, and uncover growth opportunities.

- Conduct exploratory analysis, root-cause investigations, and hypothesis-driven deep dives.

- Support engineering and research science teams in evaluating model performance, including stability, calibration, and long-term health.

Experimentation & Measurement

- Assist in designing A/B tests, computing key metrics, and interpreting results.

- Apply basic statistical reasoning (e.g., variance, confidence intervals, significance testing) to support decision-making.

Cross-Functional Collaboration

- Partner with engineering to integrate new data sources, refine data structures, and enable scalable analytics.

- Work with product and business teams to understand analytical needs and translate them into actionable solutions.