I lead all data science and AI functions at Neiman Marcus Group (NMG). I lead a team of 16 people. The team is composed of 7 US based Data Scientists, 1 ML/Ops Engineer and a Director along with a team that I built from the ground up in Bangalore India that consists of a data scientist, ML/Ops Engineer, 4 contractors and a director. In addition I have a Scrum Master that dotted lines into me that runs my org’s JIRA board. My responsibilities extend to participate in strategic planning with our VP and SVP personnel, capital planning along with presentation and stakeholder management at all levels of the business by Providing presentations of results, selling the strategic plan and answering questions from the C-Suite to leading adoption, troubleshooting issues and designing enhancements and features to help the front line business end users. My organization rolls up into the Product and Technology organization where I have active collaboration with my peers leading product and engineering where we work together to execute on a shared strategy and vision.

I led a group of data scientists distributed across the United States. I divided the team into 3 functional areas. 1. Pricing Science - Problems in this space we looked at how to set pricing for private brand SKUs and SKUs without any competitors. We also optimized price for overstock and discontinued skus to not only optimize price to optimize profit, but to minimize time to clear inventory to reduce holding costs.2. Merchandising Science - Problems in this space were targeted to enhance the quality of our catalog. The team worked on problems to improve data sources to make better catalog and pricing decisions, automate processes to save man hours, among others.3. Statistics - We were tasked with establishing statistical rigor in pricing tests and experiments. We were the thought leaders in test design, establish tenants and evangelize best practices with the vision of creating a center of excellence across all Chewy.

I led a global team of data scientists, analysts and senior managers to develop, implement and manage models to optimize and allocate a multi-billion dollar marketing budget across multiple advertising channels, brands and points of sale. Our team drove alignment of key measurements across brands and owns key measurements such as attribution, customer lifetime value, etc.Our team collaborated with Engineering to build production level interfaces for our products and architect data platforms in AWS for our products to ingest data and then ultimately export out to. We partnered with Finance to help set budgets, forecasts and targets across the business. We also partnered with marketing teams to execute on the recommendations of capital allocation algorithms and collaborated on key problems to address within the marketing space to enhance our accuracy and refine signals.

I led the statistics and enablement team along with the Customer Insights team within the Expedia Global Marketing and Retail Analytics Organization. The team focused on 4 core focus areas1) Enablement – We focused on analyses that utilized technical resources across the company to do routine work and focused on how we could automate these processes such that they could either run without the normal technical resources or could be managed by non-technical personnel thus freeing up the technical resources to focus on more valuable problems. This also involved solving problems that spanned multiple groups within the company that may be getting missed since they don’t represent a large value to any single organization, but to the overall company may represent significant value. Within this team I also managed vendor relationships that supported Marketing Analytics.2) Statistics – We lent statistical expertise across groups undergoing experimentation which may range from conducting complete A/B, Incrementality or other tests to providing peer review of a groups experimental setup and results to doing econometric analysis and understanding the economics at base unit levels.3) Data Products – We led the product management for the development of data products incorporating taking in requirements and documenting deficiencies from data products being used within marketing and working with our engineering partners to design and deploy key data products to drive key marketing results.4) Customer Insights - We focused on driving insights into what defines the customer and how we can better serve the customer through marketing and across the products we offer on our site. Some key spaces that we worked on were Customer Lifetime Value, Audience Segmentation and Product Personalizations.

I led a group of 4 data scientists including myself. Together we were in charge of researching ways to increase the revenues of the company, and Seabourn cruises, utilizing statistics, operations research and other analytical techniques. Some items we work on are a joint revenue management system across 6 brands in carnival corporation responsible for generating an additional 80 million dollars plus in revenue annually across all 6 brands. As part of that system we forecast incoming bookings, model price elasticity of demand over time for cruises, optimize prices, etc leveraging the R language and CPLEX to complete optimization. We also conduct research and develop other advanced analytical tools that are responsible for increasing the intelligence of the company and increasing revenues.I often consulted on projects that other departments were undertaking that required a high level of statistical expertise, I would create machine learning models solve high impact problems, and created metrics such as one that measured the total value of a guest to the company which was used by the marketing group to enhance the ROI on those that they are targeting. As a part of this role it was incumbent on me to collaborate with our engineering and data architecture teams to make sure we had the data and architecture in place to be able to build the models we needed, and also that what we built would integrate back into the overall architecture.

I managed all data science research at Holland America Line. My primary project was the research, modeling, implementation and maintenance of a revenue management system. Some of the projects that went into this are creating models to match similar historical voyages. From those matches we created demand profiles for future voyages and created customer retention and churn models. I have also calculated and modeled the price elasticity for our different programs.I often consulted on projects that other departments were undertaking that required a high level of statistical expertise, I would create machine learning models solve high impact problems, and created metrics such as one that measured the total value of a guest to the company which was used by the marketing group to enhance the ROI on those that they are targeting.

I teach an online math class for BYU-Idaho