Electronic Arts

The Sports Security team ensures that all EA Sports products are developed with the security and gameplay integrity of our players as a top priority. We partner with both platform development teams and game studios to ensure that security and game integrity issues are identified and resolved throughout the application and service lifecycle.

You will be a member of the Sports Security Data team reporting to the Lead Data Scientist. You will work with the team in developing excellent models to keep our games fair, fun, and free of cheaters.

We support hybrid work, and you will work two days per week in our Vancouver office.

Responsibilities:

As a Data Scientist on our Sports Security Data team, you will be a part of a global security team and will work with game and central tech development teams to:

• Work with team members across multiple disciplines to understand the data behind game features, user behaviors, the security landscape, and our goals.

• Analyze data from several large sources, then automate solutions using scheduled processes, models, and alerts.

• Work with partners to design and improve metrics that guide our decisions by summarizing the state of game security.

• Detect patterns associated with fraudulent accounts and anomalous behavior.

• Solve scientific problems and create new methods independently.

• Translate requirements and security questions into data insights.

• Set up alerting mechanisms so our leadership is always aware of the security posture.

Qualifications:

• Postgraduate degree, with specialization in machine learning, artificial intelligence, statistics, or related fields, or 2+ years of equivalent work experience in applied machine learning and analytics.

• Experience with SQL and NoSQL databases.

• Proficiency in Python programming.

• Familiarity with statistics, modeling, and data visualization.

• Experience with online gaming and an interest in addressing cheating issues.

EXPERIENCE

• Experience building statistical and machine learning models, applying techniques such as regression, classification, clustering, and anomaly detection.

• Experience with Visualization tools and libraries such as Tableau, Looker, or Matplotlib.

• Familiarity with Big Data tools such as Hadoop, Spark, or Splunk.

• Familiarity with cloud platforms such as AWS, GCP or Azure.

• Some experience to software development or data engineering.

• Analyze business problems or research questions, identify relevant data points, and extract meaningful insights.

• Experience or familiarity with security concepts, including network/software security, security data, and attack vectors.