Sample Resume: Predictive Analytics Developer

Montel Ramirez
Carlsbad, California
Twitter: @soothsayer
mramirez@email.com
000.555.1212
[YouTube and SlideShare links]


Predictive Analytics Developer

Reliable Soothsayer with a proven track record of applying predictive analytic techniques to data management, producing a host of incremental revenue and profit opportunities in diverse industries on a contract basis. Comfortable using quantitative approaches, creative data analysis, algorithm development and modeling to create assumptions based on historical data and testing thresholds to predict future performance. Strengths include:

  • Quantitative Analysis
  • Decision and Predictive Analytics
  • Optimization, Simulation, Statistical Modeling
  • Machine Learning, Data Mining, Text Mining, Natural Language Processing, Information, Retrieval, Social Media Analytics
  • R, SAS, SQL Server Business Intelligence Development tools, Sybase IQ
  • Industries: Agriculture, Politics, Credit and Risk, Insurance, Non-Profit, Retail

Sample Projects

Loss Prevention: BlueBox and MoneyChanger
Description: Revenue Assurance Modeling
Impact: Reduced fraud, employee dishonesty and machine failures by 33%.
Methodology: Developed and implemented risk detection algorithms and models to identify patterns that are predictive of machine failures, dishonest work behaviors or other potential theft or fraud exposures. Loaded all relevant data according into a defined metamodel, calculated adequate quality criteria and generated cleansing reports.

Politics: Jones for Senate
Description:
Uplift or Persuasion Modeling, Microtargeting
Impact:
Boosted donations, voter turnout and volunteerism by 7%.
Methodology:
Created predictive models using historical and current data to improve and empower fundraising, volunteer and voter targeting and mobilization campaigns. Identified individuals who would be positively influenced by ads, mailings, phone calls and other outreach efforts. Created a matrix of political, demographic and household data to develop a set of predictive models that applied a score to every voter. View my Microtargeting Best Practices video. (insert link)

Retail: Catalogue King
Description: Demand Forecasting and Anomaly Detection
Impact: Increased call center output by 10%. Reduced call abandonment rates during peak times by 18%.
Methodology: Developed analytics and predictive scoring models to optimize call center operations. Created impactful business rules and KPIs through classification, regression testing, demand forecasting and anomaly detection analytics.

Agriculture: AgriData
Description: Predicting the impact of climate change.
Impact: Increased users by 25% and profits for farmers by 8%.
Methodology: Built predictive models to help farmers make better decisions and increase profitability by benchmarking and analyzing weather data, agricultural statistics, geological surveys, satellite remote sensing data and more. Utilized novel statistical modeling techniques for pattern recognition problems which reduced noise while maximizing performance and accuracy. View a detailed project summary. (insert link)

Insurance: Gibraltar Group Plans
Description: Predicting high risk groups.
Impact: Reduced claims by 8%
Methodology: Applied advanced statistical techniques to create and/or update predictive models for use in claims, pricing, strategy, underwriting, etc. Accurately predicted high risk groups for health and disability insurance by extrapolating internal and external data and tapping in-depth knowledge of SQL, GLMs, GLMMs, CART and/or other multivariate modeling techniques.

Retail: Tom’s Sporting Goods
Description: Product Placement Optimization, Margin Optimization and Demand Forecasting.
Impact: Increased sales by 15% and gross margins by 3%.
Methodology: Conducted consumer research, in-market studies and retail landscape evaluation to develop predictive models as part of a concentrated effort to improve customer reach and product placement. Created a model incorporating social media data into customer analytics. Developed a market test to determine pricing elasticity of various product offerings and collaborated with large cross-functional team to roll-out price changes.

Relevant Work Experience

  • Big Three Consulting: Predictive Analytics Modeling Consultant
  • North American Insurance: Actuarial Analyst, Predictive Model Developer
  • San Diego Water and Power: Database Developer and Data Analyst (SQL and Oracle)

Education, Publications and Presentations

  • UC San Diego: Bachelor’s Degree in Math and Statistics, Courses in Data Management
  • UC Irvine Extension: Predictive Analytics Certificate
  • Microsoft: Data Mining, Predictive Analytics with Microsoft Analysis Services and Excel PowerPivot
  • White Paper: Demand Forecasting in Retail (insert link)
  • Video: Why Predictive Models Fail (insert link)
  • Presentation: How to Create Market Basket Analysis Data Mining Models (insert link)
  • Presentation: How to Predict Emerging Customer Needs (insert link)

View my entire portfolio online (insert link)

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