Head of Global Fraud Modeling- Fraud Risk Sr Group Manager (Mason)

Compensation

: $114,816.67 - $200,320.00 /year *

Employment Type

: Full-Time

Industry

: Financial Services - Insurance



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Career Opportunity

Head of Global Fraud Modeling- Fraud Risk Sr Group Manager

Apply Now Locations: Wilmington, Delaware, Jacksonville, Florida Job Function: Risk Management Employee Status: Regular Job ID: 19144120

Role Purpose

This role will be a part of the Global Fraud Prevention/Analytics/Customer Experience Leadership team. The role helps the organization solve its more complex problems, conduct research to answer GCB Frauds business problems by turning customer, partner and employee structured and un-structured own and third-party data into insights using and developing advance analytics and modelling. This role will have ownership of Fraud Model Development with a focus towards deployment within real-time fraud detection platforms. This role will be responsible for leveraging the latest analytical and modeling tools, techniques and big data to enhance fraud detection and mitigation capabilities; included but not limited to development and deployment of traditional and advanced analytics models in real time prevention and detection platforms.

Responsibilities

  • Responsible for modeling complex GCB business problems across all LOBs (retail bank, credit cards and mortgages), discovering the organization insights and identifying opportunities through using state-of-the art methods (e.g. statistical, algorithmic, mining and visualization techniques, machine learning among others).
  • Work closely with clients from the organization to turn data into critical information and knowledge that can be used to solve key use cases
  • Act as a creative thinker to the organization and propose new ways to look at problems by using data and available information (e.g. with predictive modeling, machine learning, etc.) presenting back their findings to the business by sharing their assumptions and validation work in a pragmatic / simple ways that can be easily understood by their business non-analytics counterparts.
  • Lead the discovery process with Global Fraud Leadership, Regional Counterparts and key stakeholders to identify and suggest solutions to problems or possible opportunities to the business.
  • Responsible for automating work through the use of predictive and prescriptive analytics.
  • Lead and develop an internal capability to distill information and results from machine learning and other data science models into something that is simple and pragmatic and that everyone can understand (e.g. storytelling to key stakeholders based on analysis and experiments).
  • Oversee a team that builds, tests, and analyzes data models, artificial intelligence, and machine learning tools, to enhance user experience, customer service, and product innovation.

Knowledge/Experience/Technical skills

  • Education: Bachelors, Master Degree or PhD degree in Quantitative Discipline (i.e Statistics, Math, Computer Science, Physics, Engineering, Operations Research, Analytics, or other related field.

  • More than 10 years of experience in relevant quantitative and qualitative research and analytics.

  • Experience with large scale Fraud organizations within financial services or related industry
  • Strong understanding of various Fraud systems such as SAS Raptor, Actimize, FDR DefenseEdge, PinDrop, etc., spanning across authentication, detection and resolution domains
  • Ability to devise solutions to loosely defined business problems and identify untapped opportunities by leveraging data.
  • Strong business acumen with ability to apply analytics techniques in identifying fraudtrends quickly and accurately to mitigate business risks
  • Be able to apply sophisticated analytics programs (e.g. statistical and predictive modeling techniques including machine learning) to build, maintain and improve fraud prevention, customer experience and operational expenses, concurrently.
  • Experience working in Big Data environment with hands on coding experience within various traditional (SAS, SQL, etc.) and open source (i.e. Python, Impala, Hive, etc.) tools.
  • Proficiency in various quantitative, optimization and predictive analytics using various statistical techniques
  • Excellent understanding and hands on working experience of traditional and advanced machine learning techniques and algorithms, such as Logistic Regression, Gradient Boosting, Random Forests, etc.
  • Experience with data visualization tools, such as Tableau, Excel, etc.

Personal Skills

  • Excellent written and communication skills to report back finding in a structured, clear, simple way.
  • Experience in leading teams of Data Scientists.
  • Inspirational Leadership.Ability to exercise sound judgement and decision making on behalf of the organization.
  • Excellent planning and organizational skills
  • Passionate about data science and the value that can be created through information analysis.
  • Energized by asking what if, why not, and what would happen to us if questions; e.g. how will this will affect the business, industry, or market.
  • Ability to manage analytic/technical professionals successfully and communicate analytic results to non-technical partners.
  • Able to be independent and a self-starter, comfortable in a fast-paced matrixed and ambiguous environment.
  • Strong execution skills and results oriented approach.
  • Act with curiosity and takes the initiative to identify problems, offer creative solutions and resolve issues.

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Grade :All Job Level - All Job FunctionsAll Job Level - All Job Functions - US

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Time Type :Full time

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Citi is an equal opportunity and affirmative action employer.
Minority/Female/Veteran/Individuals with Disabilities/Sexual Orientation/Gender Identity.

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Apply Now


Associated topics: actuarial consultant, actuarial director, actuary, actuary consultant, cost, director actuary, investment actuary, life actuary, model, retirement actuary * The salary listed in the header is an estimate based on salary data for similar jobs in the same area. Salary or compensation data found in the job description is accurate.

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