Within the School of Computing Science, I teach a Masters program course I developed: "Data Science in Business", to help technical students become well-rounded analytical data scientists.

I lead a team of data scientists whose mission is to uncover predictive customer insights from a wide range of data - transactional, web browsing, email behaviors - and deliver, test and validate through multiple marketing channels; Email, Web, Paid Search. The focus is on customer audiences through key aspects:Incrementality measurement: A/B testing, Causal Inference, Synthetic controlsPredictive platforms: predictive insights on hundreds of dimensions; product categories, product gender, activities, colors, fabrics…Customer segments and attributes: customer segmentation, gifting behaviorPromotion optimization through uplift modelingBuilding relationships throughout the Digital Marketing and Ecommerce organizations, I grew the scope of the team from a single channel stakeholder to utilizing our predictive customer insights into multiple channels and functional areas.

• Oversee the analytics behind the member contact plan: attrition predictive model, member segmentations, tactical leads, campaign tracking and analyses• Defined member primacy, identify the relevant strategies to grow, and support the operationalization and tracking for front line staff• Lead the design and the build of the “Next Best Action” engine, comprising of rules-based and predictive models – currently used in various campaigns• Identify opportunities to leverage new data sources and action them – for strategic analyses or operational improvements• Optimize data and operational processes allowed by my knowledge of broad functional areas and their relationships• Coach and mentor analysts, data engineer and peer managers

I brought predictive modelling and segmentation expertise to the Enterprise Intelligence department where I built predictive models and produced actionable insights for various lines of business (Marketing, Sales, Credit, Insurance…) using data from multiple sources; banking system, credit reporting, socio-demographic segmentations. in particular: - Attrition model (building and validating target, model, deploy) to be used across the membership. - Credit Card model used to identify best cross-sell opportunities; when deployed the model yielded a 15% sales rate - without promotion.

As part of the Epidemiology and Population Health department, I lead a team of talented data analysts. My expertise and responsibilities included: - Overseeing design and development of multiple data analyses on survey, clinical and healthcare administrative data; availability of data, choice of metrics and measurements... (Oracle SQL, SAS). - Advising and executing data retrieval and management of databases for cohort-based epidemiological and clinical studies (CANOC collaboration, CHIWOS Study) - Strategy and implementation of data linkages and integration of multi-institutional clinical and administrative databases with partners such as PopDataBC, BC Ministry of Health, BC Vital Stats, BC CDC, BC Cancer Agency... - Creating and presenting research and technical reports (posters, oral presentations). - Overseeing data quality and automation of recurring processes. - Setting up standards in privacy and confidentiality; help define the Privacy Policy, identify potential issues... - Contributing to multiple peer-reviewed publications

I developed multiple data analyses for clinical and administrative data research (Oracle SQL, SAS).

I worked on transactional data from various US and European supermarkets:• Shop Styles Customer Segmentation: I used basket analyzers, products grouping, factor analysis and clustering techniques to create shopping style segments, allowing a better targeting of customers (Families, Convenience, Traditional cooking, Health and organic…).• Key Value Items: I identified a list of 500 key products to prioritize as part of the marketing strategy; based on price, customer preference, penetration and customer priority metrics.• Custom Segments Monitor: I built a custom reporting tool for custom segments: key metrics over time, segment migration and metrics by demographic profiles. The tool was an automated SAS process integrated with Excel.

As an R&D Statistics and Modelling Analyst, I was in charge of a radio-mobile forecasting application. I designed the application and the analytical core, and was supervising the development of the UI. In this pre-smartphone era, I also worked on predicting road traffic information from mobile traffic data.