If you’d like to discuss a project or general consulting work, feel free to drop me a line. I operate independently via GormAnalysis LLC based in the New Orleans area. My rate is $90/hr but I’m open to a variety of pricing structures.



I have a successful track record of doing exploratory data analysis and predictive modeling for clients ranging from retail businesses to marketing agencies to insurance companies. My general approach to this is

1) Phone call or Skype session with client to discuss project goals
2) Review of client’s data to determine magnitude of the project and whether project goals are feasible
3) If goals are determined to be feasible, a contract is put in place and work begins

From here, the process is very iterative as I work with the client to extract useful information from their data and/or build a predictive model. For context, this includes


– Visualizing the geolocation of client accounts and service teams
– Identifying common complaints and keywords in customer reviews
– Ranking attributes of a sales lead which drive the conversion of a sale
– Identifying patterns and clusters of retail customers’ purchases and behavior


– Predicting which customers are likely to churn
– Predicting which sales leads are likely to convert
– Predicting the expected loss rate of an insurance exposure
– Optimizing a customer products for a “flavor of the month” subscription service
– Estimating Customer Lifetime Value


I’ve provided clients with a range of deliverables including Python code, R Code, R Shiny based applications, algorithm blueprints and one-on-one consulting. Please browse the Projects section on the homepage for samples.

Ben Gorman Portrait (credit to

About Me

I’m Ben Gorman – math nerd and data science enthusiast based in the New Orleans area. I spent roughly five years as the Senior Data Analyst for Strategic Comp before starting GormAnalysis. I love talking about data science, so never hesitate to shoot me an email if you have questions.

As of January 2017, I’m a Kaggle Master ranked in the top 1% of competitors world-wide. (Competitive machine learning for those who are unfamiliar.)

LinkedIn  ~  UpWork ~  Kaggle  ~  StackOverflow  ~  GitHub


  • 60% R, 30% Python, 8% Excel, 2% SQL
  • Libraries/Packages: data.table, ggplot2, randomForest, XGBoost, numpy, pandas, scikit-learn, matplotlib
  • Macbook Pro w/retina display (2.3 GHz Intel Core i7, 16 GB Ram)


  • Forecasting
  • NLP and Text Classification
  • Retail & ECommerce Science
  • Direct Mail & Marketing Optimization
  • Pattern Recognition and Trend Analysis
  • Business Intelligence and Reporting
  • R & Python Programming