1. Ronald Tharp
New York, NY
Mobile: 617-835-5432
Email: RTharp2014@gmail.com
Primary Qualifications:
• Professional experience with application coding in Python and C++ both individually and as part of a team
• Experience in backend development for high volume web applications using frameworks such as Django
• Experienced in data analytics, machine learning, statistical methods, SQL, SAS, SPSS, MATLAB, Hadoop
• 4+ years of experience with predictive analytics and statistical analysis for business intelligence, sales, and marketing
• Experienced working with AWS, RDS and S3 for web services and large data analytics
Experience:
Kinnek - New York, NY February 2016 – Present
Software Engineer
• Developed unit conversion and rounding framework to create rules for auto quoting specific product requests. Use of
rounding rules allowed different suppliers to auto quote the same request in units and quantities that were applicable to
them. Reduced number of messages required to create a sale by 15%.
• Transitioned processing of customer messaging from web servers to workers via use of Celery task queues. Resulted in
increased efficiency of customer success team and reduced site response time
• Created interface using Django forms which allowed the sales and onboarding teams to automatically generate a
customized and fully functional fake supplier account which would have realistic and applicable quotes and requests for
quotes based on the supplier who was to be demoed.
• Transitioned our sites search functionality to haystack utilizing Elasticsearch. Reduced time to search and load on our
database. Utilization of n-gram indexing and scoring enabled fuzzy matching while also ensuring high quality results.
• Utilized Redis caching of several high load and high touch calculations to improve performance and reduce DB load.
Context Relevant - New York, NY January 2015 – November 2015
Software Engineer
• Lead developer on project for large banking client to design and implement a custom application using Python and
distributed machine learning to generate prioritized call lists around the regular announcement of major economic
events. Output from application is currently utilized in the CRM system for advisors in the Asian and European markets
• Utilized Python, C++, and proprietary platform for distributed machine learning to input hundreds of gigabytes of
transaction data; join with CRM and market data; derive first and second order interactive features; and perform logistic
regression. Final model dramatically improved recall and precision over baseline model used by client.
• Developed using Python a model to identify massive credit card data compromises based on analysis of point-of-sale
records when cross-referenced with cards which reported fraudulent activity. Back testing was highly successful with
model identify several known data compromises and some previously unidentified but likely data compromises.
• Utilized Agile development methods with Git and SVN for collaborative code development, testing and validation
Lattice Engines - New York, NY
Senior Analytic Engineer December 2011 – March 2014
• Technical lead on all projects within the asset management vertical focusing on the utilization of transactional and
demographic data to develop predictive models for the sales and marketing of emerging funds to financial advisors.
Client reported an improvement of over 50% in their prospecting revenue and hit rate
• Project manager and team leader for several projects focused on using behavioral data to better target marketing and
optimize the efficiency of our clients’ customer relationship management systems
• Worked with development and modeling teams to optimize our proprietary scoring algorithm by incorporating mutual
information and isolating high value behavioral and demographic parameters most associated with future sales
• Led the analytics and implementation of a $500K project for a major telecommunications company to improve their
prospecting and client retention through use of SQL, logistic regression, and predictive scoring

2. Booz Allen Hamilton - McLean, VA
Senior Consultant July 2008 – December 2011
• As technical lead, created a logistic regression scoring model in SPSS from over a million data samples to rank-score
potential recruits across the four major commands. Model was actively used in recruiting by all four Army commands
• Technical lead for a project that developed a Monte Carlo model for the Army to forecast the impact of different
recruiting strategies on unit readiness and determine the optimal mixture of contract lengths
• Developed a SAS-based logistic model using demographic, behavioral, deployment and psychological data to identify
violent crime risk-factors among soldiers. Results were presented to Congress by the Secretary of the Army
• Developed a model using Monte Carlo simulation to predict employee attrition at various NASA centers
• Generated a Holt-Winter forecasting model for internal attrition within different Booz Allen Hamilton divisions
Education:
Stevens Institute of Technology - Hoboken, NJ
• Graduate Certificate in Financial Engineering: Dec 2014 (GPA: 4.0 / 4.0)
o Computational Methods in Finance, Stochastic Calculus
Massachusetts Institute of Technology - Cambridge, MA
• Master of Science in Mechanical Engineering: June 2008 (GPA: 4.9 / 5.0)
o Supporting author on paper analyzing HC emission in direct injection engines
• Bachelor of Science in Mechanical Engineering: June 2005 (GPA: 4.6 / 5.0)