Statistics, predictive modeling, machine learning, Data Science,SAS, R
Resume:
CHRISTOPHER LEISNER
** ******** **., ********* ** 14616
***********.*******@*****.***
SUMMARY:
Statistical analyst with concentration in loss prediction, risk prediction, and improving business processes
Experience in data analytics and requirement gathering
Knowledge of data preparation, including acquisition, transformation, and cleaning
Proficiency in predictive modeling and data mining in R, SAS, and Emblem
Expertise in data visualization using Tableau, R, SAS, and Excel
Excellence in verbal and written communication, training, and mentoring
COMPUTER SKILLS:
SAS, SQL, Hive Query Language, Emblem, SAS Enterprise Miner, SAS Text Miner, R, Tableau
QUANTITATIVE SKILLS:
Generalized linear models
Data mining
Text mining
Data preparation
Data visualization
ACTUARIAL EXAMS PASSED:
SOA Exam P, SOA Exam FM, CAS Exam 3L, SOA Exam MFE, SOA Exam C
DEGREES:
M.S. Statistics May 2008
University of Illinois at Urbana-Champaign
Ph.D. Applied Mathematics May 2000
Purdue University
M.S. Electrical Engineering Dec 1999
Purdue University
B.S. Mathematics May 1991
State University of New York College at Brockport
CERTIFICATIONS:
Springboard – Foundations of Data Science Aug 2017
EXPERIENCE:
Eligibility and Enrollment Verification Documents Administrator, Maximus 8/17-2/18
● Researching information from documents in health insurance applications submitted to NY State of
Health marketplace
● Comparing this information from documents against health insurance applications to determine if the
documents validate necessary information
● Editing health insurance applications to match information contained in documentation, when necessary
● Generating notices to consumers who need to take follow-up action
● Training new administrators (both one-on-one training and in presentations to groups)
Test Scorer, Data Recognition Corporation 4/17-5/17
● Scoring standardized tests taken by students to comply with Common Core state standards
Business Performance Statistical Analyst, American Family Insurance 9/14-10/16
● Constructed statistical model to predict losses and expenses for automobile and homeowner lines of business
● Carried out time studies of claims workers’ time sheets to analyze their time allocation among tasks
● Constructed attorney retention predictive model to predict which bodily injury automobile claims
would have attorneys retained by claimants
● Trained employees in the use of Hadoop cluster and text mining
● Performed text mining tasks to extract information from unstructured data, such as consumer
sentiment from survey comments
● Carried out adjuster time study to reduce loss expenses by increasing adjuster productivity without
negatively impacting service
● Built claim routing models to route auto physical damage and bodily injury claims to appropriate groups
of claim handlers
● Assessed performance of vendors who auction salvaged vehicles by constructing models to predict
returns produced by vendors
● Built statistical model to decide whether or not to subrogate automobile claims
Senior Actuarial Analyst, QBE Insurance 5/13-9/14
● Built models to maximize profitability of QBE’s home inspection strategy; model scores homes
according to likelihood that inspection will reveal underpricing or major hazards
● Conducted credit study for personal auto and homeowner line of business to maximize return on
QBE’s purchase of credit data; recommended purchase strategy based upon this model
● Created model for scoring QBE insurance agencies to assess their future profitability potential
● Led initiative to assess text mining tools for use in policy classification, claim classification, and
creation of new rating factors; trained team in the use of text mining tools
● Constructed models to assess quality (as measured by prospective pure premium) of QBE’s
lender-placed homeowner insurance portfolio
● Constructed predictive models for pricing equine major medical and mortality coverage for livestock
line of business
Associate Predictive Modeler, Allstate Insurance 2/11-5/13
● Defined key business problems to be solved for increasing profits from homeowner insurance,
e.g. segmenting policyholders into risk groups
● Defined key business problems to be solved for reducing losses from claims fraud from personal
auto line of business, e.g. efficiency of flagging suspicious claims
● Gathered internal and external data for construction of mathematical models to solve these problems,
e.g. ISO data and U.S. Census Data
● Used statistical analysis to assess data sources
● Performed text mining of claim handler notes to extract information for construction of predictors for
automobile claim fraud models
● Drew conclusions regarding pricing of homeowner insurance and automated referral of claims to the
Special Investigative Unit; presented conclusions and recommendations to management
Associate Predictive Modeler, Zurich Financial Services 2/09-1/11
● Defined key business problems to be solved for improving efficiency in the handling of workers’
compensation claims, e.g. triaging claims using information available at first notice of loss
● Defined key business problems to be solved for increasing profits from commercial auto line
of business, e.g. segmenting policy holders into risk groups
● Gathered internal and external data for construction of mathematical models to solve these
problems
● Used statistical analysis to assess data sources
● Performed text mining of workers’ compensation claim handler notes to extract information for
construction of triaging model
● Drew conclusions regarding triaging of workers’ compensation claims and segmenting commercial
auto policies; presented conclusions and recommendations to management
Statistical Consultant, University of Illinois at Urbana-Champaign 10/07-5/08
● Performed statistical analyses of traffic accident data for the Illinois State Police to improve driver
and passenger safety
● Made recommendations to Illinois State Police regarding optimal timing and placement of patrols
to improve traffic safety
● Conducted statistical analyses of rainwater contaminant data for the Illinois State Water Survey
● Made recommendations to Illinois Water Survey for improved testing methods for labeling rainwater
samples as either contaminated or non-contaminated
Strategic Resources Intern, State Farm Insurance 1/07-8/07
● Constructed mathematical models for flagging automobile claims as possibly fraudulent
● Created mathematical models for predicting claim severity for automobile insurance policy holders
● Conducted research to determine ways in which State Farm could utilize new electronic wallet
technology to improve customer service; made recommendations for the usage of this technology
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