Data Scientist - Analytics

About App Lovin

App Lovin makes technologies that help businesses of every size connect to their ideal customers. The company provides end-to-end software and AI solutions for businesses to reach, monetize and grow their global audiences. For more information about App Lovin, visit:

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 App Lovin, 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.

Fortune recognizes App Lovin as one of the Best Workplaces in the Bay Area, and the company has been a Certified Great Place to Work for the last four years ). Check out the rest of our awards HERE.

We are seeking a Data Scientist – Analytics to join our team and help bridge the gap between engineering and business functions  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.

• 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.

• 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.

• 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.

• 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.

• Bachelor’s degree in Data Science, Statistics, Computer Science, Mathematics, Economics, Engineering, or another quantitative discipline.
• Basic understanding of core statistical concepts and introductory modeling techniques, with the ability to apply them in practical analysis
• 0–3 years of experience in data analytics or data science (internships or projects count).
  Proficiency with SQL and at least one analytical programming language (Python preferred).
• Ability to work with large datasets and translate findings into actionable recommendations.
• Ability to translate business questions into analytical frameworks and communicate insights effectively to both technical and non-technical audiences.
• Curious, proactive mindset with a desire to learn quickly and contribute meaningfully.

• Master’s degree in Data Science or a related quantitative discipline.
• Experience with BI tools such as Looker, Tableau, Superset, Metabase, or similar.
• Familiarity with cloud data warehouses (e.g., Big Query, Snowflake) and workflow orchestration tools such as Airflow.
• Exposure to A/B testing, experiment design, or statistical…