About ThreatMetrix ThreatMetrix®, The Digital Identity Company™, is the market-leading cloud solution for authenticating digital personas and transactions on the Internet. Verifying more than 20 billion annual transactions supporting 15,000 websites and 5,000 customers globally through the ThreatMetrix® Digital Identity Network, ThreatMetrix secures businesses and end users against account takeover, payment fraud and fraudulent account registrations resulting from malware and data breaches. Key benefits include an improved customer experience, reduced friction, revenue gain, and lower fraud and operational costs. The ThreatMetrix solution is deployed across a variety of industries, including financial services, ecommerce, payments and lending, media, government, and insurance.

About the Team

The ThreatMetrix engineering team is an international team that includes experts in device identification, device intelligence, fraud detection, real time systems, Software as a Service (SaaS) applications, machine learning, and data analytics. We are an Agile engineering team using concepts such as Scrum, Continuous Integration, self-organizing teams, and Continuous Improvement. The team is led by Andreas Baumhof, CTO and SVP Engineering, who is an internationally renowned cyber-security thought leader and former executive director, CEO and co-founder of Australian-based TrustDefender. We leverage many interesting open source packages including Hadoop, Impala, Kafka, Spark, Cassandra, Solr, Storm and many more.

About You

Threatmetrix is seeking an outgoing, curious, interdisciplinary machine learning expert that has a passion for modeling and data mining.

• Bring a combination of mathematical rigor and innovative algorithm design to create recipes that extract relevant insights from billions of rows of data to improve our fraud detection capabilities.

• Learn, develop, and apply new techniques in the intersection of math, probability, and optimization.

• Translate unstructured, complex business problems into an abstract mathematical framework, making intelligent approximations when needed to put your algorithm to work at scale.

Expected Personal Skills

• Clear and effective English communicator of technical issues

• Team player who works effectively with self-managed cross-functional teams

• Organized, methodical, and detail oriented

• Ability to work with geographically dispersed teams

• Committed to the success of their team and their company

• Collaborative

Required Technical Skills

• Hands-on analysis on large amount of historical data leading to the understanding of the data implications.

• Experience with statistical modeling, machine learning techniques and data mining algorithms.

• Basic understanding of feature engineering and variable selection. Knowledge on decision trees, regression models, PCA, and experimental design.

• Experience with Java, scripting languages (Python/Perl) and Unix/Linux platform. Familiarity with basic software design principles and best practices.

• Desired experience with distributed analytic processing technologies (Hadoop, Kafka, Spark, etc).

• Experience in fraud detection is a plus.

Experience

• MS or PhD in Computer Science (with focus on Machine Learning), Statistics, Math, Operations Research, etc.

• 2+ years of quantitative experience with large data-sets, from prototyping to business impact.