Data Scientist – Analytics

Applovin•Palo Alto, CA

About The Position

AppLovin is seeking a Data Scientist – Analytics to join their team and help bridge the gap between engineering and business functions. In this role, you will work with petabyte-scale datasets across their 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.

Requirements

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.

Nice To Haves

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).

Responsibilities

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.