Data Scientist

AppsFlyer is a cutting-edge technology company that specializes in mobile attribution and marketing analytics.

We run at a massive scale- t any given moment thousands of servers are consuming 150+ billion mobile app events, crunching our users’ data, serving requests and communicating on a massive scale.

Fueled by the AppsFlyer platform, Protect360 is the leading anti-fraud solution in the market, protecting advertisers from fraudulent traffic and saving millions of dollars for our customers.

We detect fraudulent traffic (fake installs and attribution hijacking attempts) in real time using machine learning, deep learning, and advanced AI techniques applied on massive data streams.

We’re looking for a data scientist to join our team and further improve fraud detection capabilities.
This position involves analyzing evolving fraud patterns, developing and deploying advanced prevention components, and leveraging domain expertise, data tools, and modern AI methods to stay ahead of emerging threats.

A partial list of our tech stack: Python (and its related scientific stack), Spark, TensorFlow/PyTorch, Kafka, AWS services, Airflow, Scala, BigQuery, and more

What you'll do

• Mine massive data sets to uncover fraud patterns and prevention opportunities.
• Develop and productionize large-scale ML, DL, and AI models to detect and prevent fraudulent activity.
• Translate business and product needs into scientific, explainable, and high-impact solutions.
• Design and maintain end-to-end ML pipelines - from data ingestion and feature engineering to serving and monitoring in production.
• Collaborate closely with product, engineering, and business teams to drive data-driven decisions.
• Stay current with AI and ML research and evaluate new technologies to strengthen Protect360’s algorithms.

What you have

• M.Sc in Computer Science, Mathematics, Statistics, Engineering or a related field
• 5+ years of experience in applied Data Science or Algorithmic Development, preferably in large-scale production environments.
• Hands-on experience in training and fine-tuning machine learning, deep learning, and AI models
• Strong programming skills in Python and proficiency in SQL.
• Solid understanding of data pipelines, MLOps practices, and working with huge datasets.
• Ability to mentor, collaborate, and communicate complex ideas clearly to diverse audiences.
• A fast learner, problem solver, and end-to-end owner with a strong “can-do” approach.

Bonus Points

• Experience in the ad-tech industry or with fraud detection systems.
• Familiarity with LLMs, generative AI, or representation learning for anomaly detection.