TECHNICAL SKILLS

• SQL, R Programming, SAS, Excel, Tableau, Spark, Python, C++, data warehouse, Hadoop/Hive, OpenStack, Munging, Spark, Scala
• Set top box data television tune in/ tune out data, Subscriber panel data, Contact center data, Retail Transactions, warranty claims clustering, segmentation, lift models, direct mail, logistic regression, propensity models, time Series analysis, forecasting, quantile Regression, recommendation systems, affinity, visualization

PROFESSIONAL EXPERIENCE

Confidential, Herndon VA

Senior Director - Predictive Analytics

Responsibilities:

• Reduce model scoring latency by 96% at the same time increased portfolio of models 10-fold. In database machine learning process updates model outcomes as data updates for all residential statistical models acquisition, cross sell and churn .
• $100mill growth opportunity identified through estimation of customer level share of wallet. Quantile regression method reduced need for survey based approach.
• Saved over $4.5mill using uplift/persuadables models to focus marketing efforts towards prospects most influenced by marketing. dis effort made use of RF models in R and ultimately scored in parallel on an OpenStack instance wif 1TB Ram and 48 cores.
• Developed algorithm to identify back to school prospects by blending disconnect wif geographical data. The process proved very useful for the outside sales team and direct efforts.
• Set Top Box Data
• Analytic insights of television tuning behavior contributed to over $800mil carriage agreement cost reduction, by quantifying counts of loyal audience of each channel
• Analysis of over 150billion records halp to inform a customer segmentation scheme designed classify and improve customer messaging and relevance of programing
• Market basket techniques used to identify VOD promotion opportunities
• Identified better performing TV ads through actual tuning which ads capture and hold audience beter than others
• Provided audience metrics and insights for Charter Owned news and sports content. Tableau dashboards to halp inform viewership and tuning patterns.
• Efficiency
• Developed R process to forecast on an ongoing basis over 500 time series 23 divisions 22 product states . Forecast used as basis for financial revenue model.
• Designed SQL algorithm to reduce model scoring latency by 97%. The process scores propensity models on over 30million records a day wif only minor interruptions in the 5 years its been running.
• Developed process to identify move activity based on multiple inputs process proved more TEMPeffective than 3rd party lists

Confidential, Columbia, MD

Associate Director

Responsibilities:

• Helped define, measure, and prioritize retention opportunities for a $10.1B insurance business.
• Managed a team to execute ongoing retention program, resulting in $7mill improvement in revenue. Hands on use of Netezza and SAS to identify and execute opportunity built on post transaction retention and revenue.
• Acquisition Strategy
• For a $17B Mobile Phone Company, engaged in effort to target acquisitions based on response model, the executed result saved $1.5mill in marketing expense while maintaining total activations. Used SAS to build an enhanced acquisition model.
• Reactivation Strategy
• For a $4.3B Retail client, conducted statistical analysis on transactional data to identify opportunities to re­ engage dormant customers.
• Designed promotion strategy to generate a $500k in new revenue. Used SAS to develop and execute a cloning model targeted towards those most like the most valuable customers.

Confidential, Richmond, VA

Senior Manager - After Market Warranty

Responsibilities:

• Increased profitability by $2.2 million wif zero investment by using modelling and targeting solicitations. Used SAS to create predictive model and SQL Oracle to execute solution
• Increased profits by $800 thousand while increasing revenues by $2 million wif price optimization. Used SAS to model demand tan optimized result for maximum revenue.
• Improved marketing performance through expanded offerings, and up selling, resulting in $3 million in profit. Identified opportunity using SAS and SQL, outlined investment, modelled return, secured funding, and oversaw execution
• Championed infrastructure development needed to manage, analyse and report warranty portfolio from an earned perspective to capture $3 million dollars in sleeping capital.

Confidential, Dulles, VA

Principle Analyst

Responsibilities:

• Developed and evangelized lifetime value model, dis tool identified $17 million non contributing subscribers. Wrote SQL (Netezza) and SAS code used to automate scoring, update model and reporting.
• Developed campaign profitability measurement tool to monitor current and projected campaign performance
• Developed SQL for reporting datamart ensured monthly update
• Trained business users how access and use table wif familiar tools (Excel)
• Capitalized on a $12 Million profit using subscriber level profitability to better inform acquisition modelling
• Developed conditional logit model (PROC MDC) to predict likelihood to respond and likelihood to pay
• Developed process to capture value resulting from call centre interactions which provides insight on how to garner the greatest return from the activity
• Developed SQL and SAS code to extract and report post contact economics
• Developed activity based view of business to monitor and drive accountability