Data Scientist - Analytics

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.

Minimum Qualifications

• 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.

Preferred Qualifications

• 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., BigQuery, Snowflake) and workflow orchestration tools such as Airflow.
• Exposure to A/B testing, experiment design, or statistical evaluation.
• Understanding of digital advertising, performance marketing metrics, or online marketplace dynamics.
• Experience collaborating with cross-functional teams (engineering, product, business).

What You'll Gain

• Hands-on experience with real production analytics, monitoring systems, and model evaluation.
• Opportunities to build foundational infrastructure that scales across teams.
• Exposure to high-scale data challenges and real-world ML performance considerations.
• A meaningful role in driving insights that inform product and business strategy.

Additional information

AppLovin provides a competitive total compensation package with a pay for performance rewards approach. Total compensation at AppLovin is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Depending on the position offered, equity, and other forms of incentive compensation (as applicable) may be provided as part of a total compensation package, in addition to dental, vision, and other benefits.

Other Types of Pay: Equity eligible

Health Insurance: Medical, Dental, Vision, Life, Disability

Retirement Benefits

• 401(k) Retirement Plan
• Paid Time Off: Unlimited Discretionary Time Off
• Paid Holidays: 10 paid holidays per year
• Paid Sick Leave: 80 hours per year
• Method of Application: Apply online

Application Window: The application window is expected to close within 30 days of the posting date.

CA Base Pay Range $156,000—$234,000 USD

• To support an efficient and fair hiring process, we may use technology-assisted tools, including artificial intelligence (AI), to help identify and evaluate candidates. All hiring decisions are ultimately made by human reviewers.
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