Contributed to the strategic/analytic consulting of all ID Analytics solutions; led pre-sales consulting engagements on credit risk, authentication/ID Verification, and fraud strategies; led non-linear modeling methodologies to clients, led credit risk model Fair Lending review for machine learning algorithms• Responsible for Fair Lending governance and MRM review of credit risk solutions with clients.• Led client engagements that drove ~$750k incremental service revenue in the first year in market.• Supported synthetic fraud product developments concepts including: go-to-market, analytic development and competitive intelligence.• Led fraud/credit risk consulting engagements that identified over $15MM in opportunities for top client.• Led analytic teams in custom analysis which resulted in $1MM revenue in one year. • Supported new solution sales which drove over $5MM in additional review in one year.• Authored article in BAI Banking Strategy – “How banks can spot and stop synthetic fraud”• Presented a break-out session on the evolution of synthetic fraud at the 2016 TRMA. Professional Recognition: two WOW Appreciation Awards from management in 2017

Contributed to the strategic/analytic consulting of new solutions for TransUnion; develop marketing insight analyses that will support client engagements; developed client proposal for analysis and custom solution development; created analytic case studies that may be used across clients; utilized risk and/or behavior segments to improve customer efficiencies• Led over 100 client engagements that drove $8MM of incremental new product revenue first two years in market. • Developed the first FCRA time-series credit data solution in U.S. Markets• Created new behavioral segmentation approaches that allowed financial services customer to target market via digital media. • Supported predictive analytics, market intelligence studies, and voice of customer evaluations that resulted in two new analytic solutions: (1) historical spend insights, (2) refined delinquency solutions. • Presented a break-out session on time-series credit data analysis at the 2014 TransUnion Financial Services Summit to over 100 executive bankers.

Supported all proactive analytic consulting for two-tier US lenders; analyzed all next-generation time series credit characteristics, statistical models, and solutions; management end-to-end development of international risk/statistical models• Managed team of data scientist which developed predictive risk models development for multiple international markets that resulted in a 7% increase to revenue. Lead management role required setting expectations to international business units, clients, managing operations of offshore team, and oversight of scorecard development. • Created the first FCRA time-series analytic solution in the U.S which resulted in $5MM in review from the first year in market. • Developed next generation algorithms that utilized historical credit data. As lead analyst the responsibilities entailed; proof-of-concept scorecard development, strategy simulations, analytic solution development, support of go-to-market materials, client consulting, and technical quality control/validations.• Developed predictive algorithms utilized new time-series credit data which resulted in up to 20% improvement to existing client strategies.• Managed File Compare Team responsible for strategic file comparisons for top banks.• Managed off-shore analytic team and team of three analysts and consultants. Responsibilities included performance evaluations, career training, and ongoing development to promote advancement.

Performed risk analytics intended to drive profitable growth; risk and return optimization techniques; analyzed historical vintages to using complex modeling or analyses; forecasted expected performance impacts to all strategy changes • Promoted to Senior Risk Analyst in 2007 and Project Manager in 2008.• Developed policy changes utilizing credit bureau risk models that resulted in an annual credit loss savings of $2.9MM, while only reducing good receivables by $12MM.• Engaged external consulting firm on initiatives that reduced acquisition cycle time by over 50%, thus improving approval rates by 6-8% and increasing accounts by 50k per year.• Contributed to optimization techniques for preapproved solicitation methods. Methods a improve PBT by $12MM on an annual basis.• Provided analysis and recommendations for credit bureau efficiency that resulted in an annual savings of $3.4MM.• Manage one-person staff, including performance evaluations, career training, and ongoing development to promote advancement. Professional Recognition: six Appreciation Awards from management and co-workers; two-time President’s Plate recipient, two-time Excellence Award recipient

Provide credit trending for C-Level management, analysis for acquisition triggers, assist in data extracts, exploratory data analysis for validation purposes, create pre-screen credit criteria• Assisted McKinsey & Company in complex exploratory analysis for credit policy recommendations. Analysis included rejected inference techniques on bureau bads.• Developed a balanced scorecard for credit criteria testing.• Created analytical designs for retro-studies. Designs consisted of timelines, required data, assumptions, and applied analytics.• Conducted multiple bureau analyses to compare differences across credit bureaus. Such analysis consisted of credit criteria performance analysis at multiple bureaus, K-S statistics of risk scores, and significance of multiple bureau decisioning.• Supported the implementation of pre-screen ITA criteria. Defined criteria at credit bureaus, and ensured criteria was in FCRA compliance.• Developed business intelligence reporting framework to monitor month-over-month credit bureau variances.• Created Statement of Work (SOW) for criteria enhancement project. Proposal included: timelines, resource requirements, assumptions, and testing roles/responsibilities.  Professional Recognition: five Appreciation Awards from management and coworkers; one Personal Accommodation from EVP; President’s Plate recipient, Excellence Award recipient

Provide credit trending for C-Level management, analysis for acquisition triggers, assist in data extracts, exploratory data analysis for validation purposes, create pre-screen credit criteria• Assisted McKinsey & Company in complex exploratory analysis for credit policy recommendations. Analysis included rejected inference techniques on bureau bads.• Developed a balanced scorecard for credit criteria testing.• Created analytical designs for retro-studies. Designs consisted of timelines, required data, assumptions, and applied analytics.• Conducted multiple bureau analyses to compare differences across credit bureaus. Such analysis consisted of credit criteria performance analysis at multiple bureaus, K-S statistics of risk scores, and significance of multiple bureau decisioning• Supported the implementation of pre-screen ITA criteria. Defined criteria at credit bureaus, and ensured criteria was in FCRA compliance.• Created Statement of Work (SOW) for criteria enhancement project. Proposal included: timelines, resource requirements, assumptions, and testing roles/responsibilities.  Professional Recognition: two Appreciation Awards from management and co-workers; one Patback Bonus Award from senior management

Develop in-house UNIX applications for process efficiency; test and debug various programs; troubleshoot DOTS Oracle database; assist in data integrity of client files; support application implementation; write SOP documentation for new applications; conduct training seminars and mentoring; data cleansing for the purpose of database loads using SAS functionality• Developed over thirty in-house UNIX applications, twenty of which produced client solutions with no employee interaction and resulted in 20% decrease in resource allocation. All completed within allotted timeframes.• Contributed to the improvement of dynamic SAS scoring models that improved cycle time by 40%.• Responsible for mentoring, training, and assisting in reviews for staff of one employee.