EXECUTIVE SUMMARY OF RESPONSIBILITIES

The Fraud Analyst/Data Scientist will develop predictive models and use proven statistical methodologies to validate fraud hypothesis. Will translate recommendations into development and implementation of sound mitigation controls involving new strategies and technologies. This role has direct impact on risk mitigation strategy of Lendistry. Success in this role requires quantitative abilities and industry and risk management experience as well as SAS/SQL/Python/R/ and knowledge ML techniques.

ABOUT LENDISTRY

Lendistry is a minority-led and technology-enabled small business and commercial real estate lender that innovates game changing lending practices for historically underserved small businesses. With Community Development Financial Institution (CDFI) and Community Development Entity (CDE) certifications, and offering SBA 7(a) loans through its subsidiary, Lendistry is a responsible partner for small businesses through their growth stages and beyond. We are a national employer whose mission is to provide economic opportunities and progressive growth for small business owners and their underserved communities as a source of financing and financial education.

GENERAL RESPONSIBILITIES

• The Fraud Analyst/Data Scientist will support the Fraud department by extracting, compiling, transforming, creating, and analyzing large amounts of data from multiple sources as part of the risk mitigation effort.
• As a member of the Fraud team, you will be providing the statistical evidence (and evaluating the conceptual soundness and performance) of new doctrines, strategies, models, technologies, and procedures to improve fraud detection efficacy and efficiency. 
• Help to validate the proof of concept of top of the industry customer authentication solutions to mitigate risk and to improve customer’s experience.
• As a member of the Fraud team, you will help to identify and to evaluate the predictiveness of traditional and non-traditional variables and sources of data in order to incorporate them into existing fraud mitigation policies, procedures, controls at Lendistry. 
• Actively participate in partner meetings (internal and external) in order to exchange current fraud mitigation recommendations as well as providing and receiving feedback regarding existing controls and technologies. 
• Design and maintain fraud data repositories and ETL process in order to ensure accuracy and validity of fraud models policies and procedures. 
• Create fraud dashboards for all fraud types.  

PROFICIENCIES 

• Excellent verbal and written communication skills. 
• Background in fraud detection / Risk Management and data science
• Ideal background in open-source fraud solutions, claims based fraud detection and abnormally detection algorithms. 
• Familiarity with fraud detection technics (rule-based, statistical/predictive models, AI supervised and unsupervised learning)
• Strong business and data analysis skills
• Experience with Python, Tensorflow, and H2o packages.  
• Experience working with large datasets as well as unstructured data. 
• Basic experience developing automation including web scraping 
• Strong communication and interpersonal skills
• Experience with drafting business requirements or business use cases specifically related to fraud detection services
• Experience evaluating our source vendors and developing a proof of concept to illustrate the technical solutions proposed.
• Sophisticated word processing and computer database skills, especially in Microsoft Word, PowerPoint, and Excel.
• Ability to think creatively and innovatively. 
• Good interpersonal skills with the ability to work effectively with individuals and groups at all organizational levels, ability to work independently and as part of a team.
• Strong analytical ability with active listening skills.

EDUCATION AND EXPERIENCE

• Bachelor’s degree in a quantitative field: engineering, math, statistics etc. MS/PHD preferred
• 3+ years of experience in business analysis, customer segmentation, and/or predictive modeling, preferably in the financial services industry
• 2+ years in using machine learning packages such as Python, R, SAS