Equifax empowers businesses and consumers with information they can trust. With a strong heritage of innovation and leadership, we leverage our unique data, advanced analytics and proprietary technology to enrich the performance of businesses and the lives of consumers.

Equifax is looking for a Statistical Consultant/Data Scientist to join our world-class Global Identity and Fraud Analytics team.  In this exciting role, you will have the opportunity to work on a variety of challenging projects across multiple industries including Financial Services, Telecommunications, eCommerce, Healthcare, Insurance and Government.  In this position you will:

  • Work with various stakeholders such as Business Consultants, Senior Statisticians, Product Management, and Clients on the formulation and application of new modeling solutions for a variety of industry problems
  • Manipulate large data sets, integrate diverse data sources, data types and data structures  into solutions
  • Develop analytical approaches to meet business requirements; this involves translating requests into use cases, test cases, preparation of training data sets and iterative algorithm development
  • Research new and advanced predictive modeling techniques as appropriate for a specific solution
  • Present results and recommendations to internal and external customers
  • Work with cross-functional teams to develop ideas and execute business plans
  • Develop solution prototypes and help integrate them with the product
  • Work closely with software development teams to communicate requirements and ensure quality of end-to-end deliverables of developed analytical solutions
  • Contribute to the team knowledge by keeping up with the state-of-the-art in machine learning applied to our domain

Desired Skills and Experience:

  • 4-7 years professional level  quantitative analytical experience, including conducting hands-on analytics projects using generalized regression models, Bayesian methods, random forest, gradient boosting, neural networks,  support vector machine, clustering, and similar methodologies
  • Proficiency skill in hands-on data mining and modeling projects with Python,  R, and SQL
  • Master’s degree or higher in Mathematics, Computer Science , Engineering, Operations Research, Statistics or other related discipline
  • Strong consultative acumen and ability to understand complex analytical solutions
  • Ability to communicate complex quantitative analysis in a clear, precise, and actionable manner
  • Ability to create new ideas for analytical solutions to address customer's business issues

 Preferred Skills and Experience:

  • 3+ years’ experience with R, Python or a similar statistical tool
  • Experience with state of the art machine learning algorithms such as deep neural networks, support vector machines, boosting algorithms, random forest etc. preferred
  • Experience conducting advanced feature engineering and data dimension reduction in Big Data environment is preferred
  • professional experience as a data scientist or statistical modeler in identity and fraud, credit risk, telecommunications, financial services, payment, ecommerce, B2B or B2C, marketing, insurance, or security analytics arena is a plus
  • Strong SQL skills in Big Data environment (Hive/ Impala etc.) a plus
  • Proficient with any programming languages such as Python , Java, Scala a plus
  • Experience working with very large datasets, knowledge of distributed computing tools (Hadoop Streaming, MapReduce, Spark) a plus
  • Exposure to Visualization tools such as Tableau a plus
  • Extensive knowledge in fraud prevention methods and detection tools a plus
  • Strong knowledge of credit bureau data and business problems in financial services and/or telecommunications a plus
  • 2+ years’ experience with SAS and proficiency in SAS Macro is a plus
  • Experience working in UNIX environment preferred

#LI-JD1

Primary Location:

USA-Atlanta-One-Atlantic-Center

Function:

Function - Data and Analytics

Schedule:

Full time
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Confirmed 23 hours ago. Posted 30+ days ago.

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