Director, Fraud Data Science - First Data Corporation Washington, DC, USA Bookmark Share Print 174 0 2

Listing Description

Interested in stopping payment fraud in a way that’s never been done before? First Data Corporation’s Global Cybersecurity and Fraud Organization is currently looking to expand its role in the cybersecurity and fraud management by offering an intelligence-driven fraud product suite to bank and merchant clients that has already contributed to millions in fraud loss averted. This position will lead the analytic development efforts and will report to the VP, Threat Intelligence & Analytics.

Description

We are currently seeking someone with the following qualifications:

• Curiosity and an interest in continued learning and teaching others

• Data-oriented personality

• Bachelor’s degree/Masters/Ph.D. in computer science, information science, computer engineering, mathematics, or related field

• 6+ years experience in information security, analytics or academic environments

• Proficiency in at least one of the following: R, PHP, Python

• Proficiency in structured or unstructured database types and query languages, including but not limited to SQL, MongoDB and Cassandra, HBase, Hive and Pig

• Proficiency with common data science toolkits, such as R, Python, MatLab.

• Understanding of machine learning techniques and algorithms, such as k-NN, Naive Bayes, SVM, Decision Forests, Hidden Markov Models.

• Experience with data visualization tools, such as D3.js, GGplot, Kibana

• Good applied statistics skills, such as distributions, statistical testing, regression

• Good scripting and programming skills to integrate solutions into existing enterprise systems

• The ability to communicate complex concepts in a clear and concise manner

• Strong organizational skills for documenting research and development methods and approaches

• Familiarity with or a strong interest in learning basic concepts of financial fraud, the financial ecosystem and threat intelligence.Processing, cleansing, and verifying the integrity of data used for analysis

Analyzing large sets of data (10s to 100s of millions of rows) to develop new models to identify potential fraudulent behavior

Developing and maintain analytics infrastructure

Acting as integral part of team researching, presenting and publishing on new methods for identifying fraud using large sets of transaction and log data

Exploiting current accesses and develop new accesses to intelligence sources

Selecting features, building and optimizing classifiers using machine learning techniques based on input from domain experts across the company to identify anomalous behavior

Data mining using state-of-the-art methods applied unstructured datasets

Applying insights garnered from third party datasets and information to proprietary datasets

Briefing results and progress to senior leaders across the enterprise.