Risk Management Statistical Modeler
Identify opportunities to apply statistical
analyses to improve business performance. Develop
and implement statistical models that enable
the company to efficiently and effectively monitor
performance of the US and Canadian lease, balloon
and commercial portfolios.
-Assist in the design, development and maintenance
of predictive models for consumer credit portfolio
used for forecasting probability of defaults
(PD), loss given default (LGD) and exposure
at default (EAD) based upon customer payment
and delinquency behavior, credit profile and
other portfolio and market dynamics
-Partner with Accounting and colleagues within
Risk Management in establishing adequate credit
loss provisions for US and Canadian consumer
portfolios; conduct sensitivity tests on the
models to accommodate qualitative overlays related
to the economy, used car market volatility,
changes in credit policies and collection strategies
and other factors that may affect future portfolio
performance; work with internal and external
audit partners to gain acceptance on the loss
reserving methodology and provide technical
support in order to meet audit and regulatory
-Assist in the validation of US and Canadian
internal credit acquisitions scorecards though
the use of KS-statistic (or similar metrics
such as ROC curve, Gini coefficient and Divergence),
population stability index (PSI), characteristic
analysis and bad rate analysis; perform similar
task on back-end/behavior scorecards as needed
-Support the on-going Data Warehouse restructuring
project through testing (i.e. validating mapping
logic and table relationships); work with IT
and Data Governance Committee to identify and
resolve data quality issues; maintain and develop
various databases, technical models and reports
required to support research, data gathering
and analytical responsibilities
-Assist in the maintenance of the commercial
portfolio’s dealer rating system including
validation of model performance (i.e. PD forecasts
and ability to rank-order accounts), update
of seasonal indices and recalibration of forecasts;
support the Commercial Credit Risk team on future
model updates and redevelopment
-Take lead in the development and implementation
of a forecasting model to quantify value-at-risk
relating to interest rate exposure using Farin’s
Foresight software or similar packages; establish
a partnership with Treasury and Accounting &
Controlling group to collect required financial
data and review validity of model assumptions
-Help improve existing residual value setting
model; perform elasticity, sensitivity and scenario
analysis using Black Book, ALG, Auctionet and
other third-party and internal data to identify
residual value strategies that will optimize
results; provide analytical support relating
to lease pull-ahead, extensions and similar
marketing initiatives to help business leaders
make informed decisions
-Collaborate with other team members to determine
if quantitative analysis and modeling is required
to improve business performance
-Educate colleagues on proper statistical principles
and methodology; sell ideas and conclusions
in a clear, concise manner to colleagues with
limited statistical background
-Identify areas of opportunity where statistics
can improve business results or increase process
efficiency; demonstrate effective planning,
research and implementation of statistical models
-Provide inputs to the Statistical Modeling
group to ensure projects and initiatives are
aligned with Risk Management’s strategy
-5+ years related professional experience is
required for this position
-3 Years Experience in a statistical field
-3 Years Working in a business environment
-2 Years Data Analysis or Modeling with large
-Bachelor Degree in Statistics, Applied Mathematics,
Econometrics, Operations Research or similar
quantitative field of study is required
-Analytical and conceptual thinking - using
logic and reason, creative and strategic
-Computer Savvy - highly skilled in software
use and programming
-Communication - excellent skills in written
and visual communication
-Advanced knowledge of concepts, principles,
standards, practices and techniques relating
to statistical modeling, analysis, interpretation
-Advanced knowledge of advanced applied statistical
methodologies including least squares regression,
logistic regression and credit scoring, sampling
methodologies, time series analysis, generalized
linear modeling, cluster analysis, categorical
data analysis, decision trees and other multivariate
-Ability to conduct large scale projects and
research through all stages: concept formulation,
definition of metrics, determination of appropriate
methodology, research evaluation and final research
-Demonstrated understanding and experience with
relational datasets, data warehouses, data mining
and data analysis techniques
-Excellent computer skills and techniques using
multi-variant statistical analysis packages
(SAS, SPSS, Knowledge Studio) and other reporting
and data analysis tools (Business Objects, Access,
-Established data mining skills using existing
databases, third party-provided data, or creating
your own when necessary
-Ability to “think out of the box”
and use unconventional methods where necessary
-Specialized ability to communicate highly technical
subjects to the senior executives and others
who have limited background in mathematics or
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