● Developed the data roadmap and worked with business partners to implement policies for robust information management practices, ensuring the quality, appropriate use, and integrity of data throughout its lifecycle.● Exceeded financial targets directed at mitigating fraud loss, while maintaining technology and operational costs stay level.● Applied statistical analysis and predictive modeling techniques to address business challenges and identify opportunities in the fraud models.● Lead the design, development and use of advanced analytical models, machine learning algorithms, and artificial intelligence applications to extract and deliver valuable insights from complex data sets.● Determined used cases for how to leverage data science, machine learning, and other analytical techniques in building a short- and long-term program roadmap for increased go-to-market in our fraud strategy.

● Eliminated two legacy analytical tools, then reduced the automated reporting footprint for a cost savings of 2.4M yearly● Modernized the legacy data pipelines from our source systems to from the on-prem analytical servers to an Amazon cloud infrastructure for 4 separate business units.

● Created a cannibalization analysis dashboard in Tableau using BigQuery Datasets to show how retail price families are affected by cross product promotions based on PSI value for price elasticity. ● Integrated Competitor Intelligence data with pricing sales history to highlight performance of previously forecasted machine learning models to show model fit and pricing accuracy.● Restructured Data Science and Analytical teams to work in pods using Agile methodology which reduced SaaS Implementation timeframes of our technology by 6 weeks and eliminated unplanned client budgetary overages.● Managed a growing software engineering team on 3 continents, and their leaders, who gathered requirements and supported product development and campaign launches.● Engaged with multi-disciplinary teams (and 3rd parties as need) to leverage data from various platforms in order to develop insights and actionable recommendations for strategic next steps.● Partnered with the CTO to continuously seek innovative ways to improve the quality and accessibility of our analytical framework and infrastructure.

● Continuously delivered advances in the retail SaaS product by executing enhancements in accordance with the product development roadmap. ● Led gap and root-cause analyses to identify areas for improvement and propose targeted solutions. While emphasizing storytelling, QBRs/ KPI’s, and actionable insights.● Focused team knowledge and analytical models on the understanding and implications of pricing, promotions, and marketing spend. Then targeted those factors for revenue growth in acquisition and client retention with cross functional stakeholders.

● Created Fraud department from the ground up to support all payment related needs including regulatory adherence, default monitoring, collection processing analysis, and fraud performance reporting. ● Increased use of available third-party data combined with targeted time studies for high ROI areas to reduce production staff levels by 13%.● Managed server consolidation deployment in conjunction with third party vendor implementation for a 26% annual cost savings.● Lead teams of data & analytics engineers that build, manage, and support enterprise data warehouse and analytics technology infrastructure, tools, and products.● Increased report automations from less than 10% to over 80%.● Developed new default time series models to predict workflow volume more accurately by factoring in multiple variables and banding state and federal regulatory guidelines where the processes are similar in nature. ● Defined the overall data strategy and architecture, including implementing A/B and multivariate testing, for the organization

● Increased overall revenue 7% for REO properties by updating and enhancing our repair evaluation models focusing on reducing 3rd party fraud through vendors.● Partnered with Data Operations teams to define technical requirements for data ingestion and data quality methodologies.● Re-tooled existing Machine Learning default models to achieve an overall accuracy of 89% for forecasted revenue projections relating to short sale homes.● Created best practices and standards for Data Integration, transformation and consumption.

● Designed and mapped the data architecture for 23 different business units to provide multiple on-demand data marts with Spotfire visualizations for easily read dashboards.● Provided technical oversight to Solution Architects in creating business driven solutions adhering to the enterprise architecture and data governance standards.● Identified and corrected deviations from database development standards. Planed and installed upgrades/ patching of databases.● Performed transformation of logical data architectures into physical data designs on large or complex systems, according to database design best practices and standards● Managed holistic roadmap of architecture change initiatives across the REO Business, coordinating and aligning requirements across different initiatives.

● Re-designed ETL processes to seamlessly integrate existing software and third-party data using a combination of Globalscape MFT and SSIS.● Worked with the Analytics team to align the conceptual and logical models.● Defined the data architecture framework, standards, and principles, including modeling, metadata, and security for projects ranging from Proof-of-Concept, to dev/test/production environments. Designed data pipelines for enterprise databases, data warehouse systems, and multidimensional networks.

● Reduced loan center purchasing expenditures by $65K yearly by moving the process and accounting controls into SQL Server with an MS ACCESS GUI to submit requests.● Decreased loan qualification process from 25 steps to 8 resulting in elimination of 2 full-time employees per location.● Created IT/Technology budget and managed 3rd party contracts related to vendor services.

• Lowered error rate from over 15% from taking over the group to under 1% on an ongoing basis by creating exception reports in Crystal Reports and tracking and automating the most common corrections in the SQL Database directly. • Created MS Access Loss Mitigation database with Integration of MSP and LPS data• Developed MS Access Database for Loss Mitigation Exceptions and reporting for executive level from Oracle SQL datasets in Crystal Reporting