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# Data Scientist

- **Company:** PROPERTY VALUE, INC.
- **Location:** Austin, TX, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Airflow, Amazon Web Services, Fraud Prevention and Detection, Apache Hive, Python (Programming Language), Machine Learning, Azure Machine Learning, Google Cloud, Snowflake, Apache Spark, Model Validation, Kubernetes, Apache Kafka, Data Pipelines, Databricks
- **Published:** June 25, 2026
- **Apply:** https://www.dice.com/job-detail/ed6a6a83-defc-4522-bccf-3626dafbee2d

## About the Role

This role is ideal for a data scientist who is analytical, curious, collaborative, and comfortable working with complex data in an adversarial environment where player behavior and abuse patterns evolve over time., * 5+ years of experience in data science, machine learning, applied statistics, risk modeling, security analytics, fraud detection, or a related analytical field.

 * Strong experience with Python or R and advanced SQL.
 * Experience building statistical or machine learning models using large-scale behavioral, event, transaction, account, or telemetry data.
 * Strong understanding of model evaluation, including precision and recall tradeoffs, false positive analysis, thresholding, noisy labels, and delayed outcomes.
 * Ability to work cross-functionally with engineering, product, operations, security, or game teams and explain complex analytical findings clearly., * Experience in gaming, gameplay security, anti-cheat, fraud detection, trust & safety, cybersecurity, bot detection, or another adversarial domain.
 * Experience with one or more of the following areas: cheating, botting, account boosting, mass account registration, fake account detection, account abuse, payment fraud, chargebacks, or abnormal gameplay behavior.
 * Experience developing features from behavioral, transactional, registration, account lifecycle, or gameplay telemetry data.
 * Experience partnering with engineering teams to operationalize models, detection signals, dashboards, monitoring workflows, or data pipelines.
 * Familiarity with cloud, data, or ML platforms such as AWS, Google Cloud Platform, Spark, Databricks, Snowflake, Hive, Kafka, Airflow, Kubernetes, or similar technologies.

## Description

* Lead the data science work across the model lifecycle, from exploratory analysis and feature development through evaluation, monitoring, and partnership with engineering on deployment.
 * Design and develop statistical and machine learning models to detect cheating, botting, account boosting, mass account creation, account abuse, payment fraud, and other suspicious gameplay or platform behaviors.
 * Build and improve features, risk signals, and detection approaches using gameplay telemetry, player behavior, account lifecycle, registration, transaction, and game event data.
 * Continuously evaluate and improve detection effectiveness by measuring model performance, reducing false positives, and adapting to evolving abuse patterns.
 * Collaborate with game teams, security engineers, product partners, fraud stakeholders, and anti-cheat teams to support data-informed detection and enforcement strategies.
 * Communicate analytical findings clearly to technical and non-technical stakeholders, including model performance, limitations, tradeoffs, confidence levels, and recommended actions.


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