The firm focuses on blending prescriptive and cognitive Analytics with progressive business strategy. Concentrating in the Financial Services, Healthcare, Public Sector and Energy.MUFG Bank - Developed Tableau dashboards to track resource project allocation over time. Built Python scripts to help automate ETL processesWarner Brothers Discovery - Created a baseline of engagement after controlling for the following: seasonality (weekly, yearly, major holidays, day of the week, week of the month), removing outliers (extreme increases or decreases), content specific trends based on title releases, other external factors (e.g.., COVID positivity rates, competitor release schedules)Amazon - Developed custom SQL to build advanced Excel reporting for the Base Pricing leadership teamState of Missouri Department of Education - Developed Tableau data flows and visualizations for the Missouri DESE Data team and deployed to Tableau Server and visualizations utilized complex mapping, dashboard actions, data blending and dynamic URLs https://apps.dese.mo.gov/MCDS/Visualizations.aspx?id=22Blue Cross Blue Shield Association - Built new Financial Database in SQL Server - building tables, views and stored procedures to business specifications and custom dashboards with managed permissions across plans, providers and physicians using SQL Server, SSIS packages and Tableau Desktop for each study using Live and Extract data sourcesVizient, Inc - Gathered over 30 disparate data sources, identified reporting readiness gaps based on business need and developed a series of Access databasesPanera Bread- Built and deployed daily sales forecasts at the store and channel level which which were used in the overall labor and food supply forecasts.McDonald's - Developed a global forecasting tool that generated adjusted trendlines by accounting for seasonal and economic factors and forecasted sales and visits 2 years into the future using 3 different forecast methods

Helping Analytical Professionals Identify and Obtain New Opportunities to Continue their Career Trajectories:Focus Areas:Predictive AnalyticsData ScienceMachine LearningBusiness Intelligence

Led all analytical efforts to support and grow the Wealth Management business which has over $200B in AUM- Introduced new third-party information within the organization to help identify over $100MM in asset opportunity amongst deposit only clients that were incorrectly moved to a low touch service model.- Developed client segmentation based on current and potential client value - identified $200MM in fee revenue opportunity with a focus on “Protect” and “Expansion” strategies- Identified key drivers of satisfaction via the 2014 client survey across region and segment - Clients that experience a problem are less satisfied and are least likely to recommend Northern. However, when the problems are solved, satisfaction, recommendation and return business likelihood all increased. Overall, for one-point increase in satisfaction, probability to transfer increases 600 bps, on average.- Built transactional database to help finance understand cash and security flows in and out of the organization – using the data, actual leverage ratios were calculated to reflect the true strength of the business and helped senior management to draw correlations between net flows and economic events over time

Increasing Penetration and SOW in All Commercial LOBs-> Implemented a robust high value prospecting (HVP) model in Illinois for Business Banking-> Uncovered significant CRA opportunities for Small Business lending-> Completed RM coverage optimization to help identify under and over served areas in terms of customer management and prospect development-> Created a new Small Business High Value Segmentation to help focus cross-sell and retention effortsAccelerating Profitable Consumer Growth in Consumer-> Leveraged Premier pilot learnings and built a transition model for all Retail customers to help prioritize Premier leads-> Provided insights to help drive and execute credit card marketing strategy-> Built mortgage models for first-time and new purchase customers to help mortgage banker prioritize leads-> Created an early tenure scoring system to improve onboarding cross-sell performance-> Measured hard and soft attrition across the bank post-conversion to help uncover segments with higher than average defection and develop future win-back/retention strategiesImproving Campaign Execution from End to End-> Increased transparency of campaign results by holding monthly review sessions with Marketing and other stakeholders-> Introduced rigor around test design – appropriate sampling with consistent no contact controls-> Continued to process list request on time given the significant increase in the number of requests and complexity-> Built competency in loading campaigns to Salesforce.com to mitigate risk once all branch teams are up and runningBuilding a World Class BI Reporting Team-> Helped our Retail LOB partners reduce their number of reports from 60 to 10 – providing a more comprehensive view all businesses.

Performing predictive and transformational analytics in the Financial Services, Healthcare, Utilities and Public Services space.-> Helped USPS cut wait time and retail staff costs in small post office locations by using transactional basket analysis and statistical modeling to identify self-service propensity.-> Built customer segmentation for Ameren Illinois to help identify marketing strategies to increase self-service channel usage. I also built propensity models to help increased eBill adoption by 167% over the next months-> Created a unique factbase of building energy usage across all buildings in Chicago. I collected the data from a variety of governmental agencies and joined the data using reference address and geo-spatial matching. I performed analysis to help idenify areas of the city that could save over $200MM in energy spend.

Used statistical modeling techniques to apply attitudinal-based segmentation to the wealth portfolio. The analysis helped to identify opportunity for wealth advisors to increase Harris' share of assets among current clients and develop talking points for prospective clients.Developed business cases for business banking direct marketing campaigns to help the marketing group justify spend. These cases utilized risk and response models along with current portfolio performance to determine the expected return and/or loss from the campaign.Conducted strategic analysis to assist the business sales force with alignment of customers to optimize the levels of customer experience, decrease attrition and increase the share of wallet for the bank.Identified growth opportunities for the Private Bank (wealth) group by determining the potential for each private bank office based on the existing client base's assets.Used statistical analysis to help the retail bank understand how dollars flow between retail and private bank accounts.

Produced campaign reporting for client's direct marketing efforts which helped drive RBS Citizens to become one of the top tier banks on the east coast.Tracked results over time and measured the correlation between market condition indices and response model performance to explain campaign performance.Created test designs to identify the most optimal offer and creative components to maximize the results of the marketing campaigns. Provided insight for home equity campaigns which resulted in improved conversation rates while maintaining low risk to the portfolio.

Assisted with the launch and implementation of the Discover's first commercial card. Led testing and selection of third-party data providers. Used consumer data to project initial launch approval rates and develop business case.Provided insight on how marketing could maximize profit and still achieve new account goalsCreated new segmentation for current cardmembers to implement active and inactive acquisition cross-sell strategies

Provided marketing with statistical consulting ranging from sample size estimation to estimating the effects of product parameters on key drivers through regression analysis Helped in the development and implementation of the innovative Balance Transfer Surfer model. This model predicted the event of a new cardmember attriting once the balance transfer promotion ended.Implemented experimental design testing to help build more robust statistical models and help with product optimization

Managed analytical support for New Account Operations to improve fraud detection and boost card activity.Provided analytical support for Acquisition Marketing initiatives, which included next generation segmentation, Scored triggers, and experimental design.Served as analytical consultant for Finance by re-engineering the acquisition NPV process. Created pricing sensitivity models for key drivers along advanced forecasting methods for later life estimation.Provided reporting and analytical support for the Telemarketing area to help increase booked accounts per hour

Developed a brand allocation model to help determine which brand to cross-sell current cardmembersValidated new acquisition risk model and its effects on future acquisition and portfolio performanceManaged relationship with credit bureaus by coordinating special tests and model sample production.

Helped to create and implement a Multi-Card Strategy model. The strategy determined which card should be offered in a solicitation based on past response and demographic information.Performed discriminant analyses on New Movers lists to determine what factors drive their higher response rates.Validated numerous response models to determine their reliability in predicting response of solicitations Created an advanced simulation of the entire Pre-Approved campaign process to optimally size test and control groups.

Developed customer predictability models to improve shareholder enrollment.Created many ad-hoc and automated reports to support the efforts of the regional sales teams.Performed complex segmentation analyses to drive effective customer solicitations.

Helped to set up new Infoscan and scanner stores for US and international businessHelped to determine the price distribution of UPCs in international markets using SAS and statistical methods (non-parametric test, ranking test, and some simple regression) Assisted in the creation of a Frequent Shopper Program response models.

Built statistical models to predict fee-income product retention