Director Statistical Model Governance
|Job Title:||Director Statistical Model Governance|
|Location:||New York, USA|
|Salary:||Up to US$200000.00 per annum|
|Contact Name:||Emily Ward|
|Job Published:||May 31, 2018 14:17|
Who will you be working for?
My client is a leading Financial Services company based in the Tri-State area who have a centre of excellence for Data Science and Analytics. They a rapidly growing the entrepreneurial department which aims to design, create and offer innovative data-driven solutions for many parts of the enterprise. they are building one of the first multivariate model-based continuous risk differentiations in the industry.
What will you be doing?
Apply your highly developed technical predictive analytics skills to review and validate a variety of models, assure proper tracking of inputs and results after implementation, create and maintain an inventory of production models and regularly test them. You will use your leadership experience, to build a team, mentor and manage employees and you will apply your experience with consumer data regulations to collaborate with the Government Affairs area on issues affecting their data and models.
Methodology review and leadership: Reviews model designs, statistical methodology and features. Explores methodology options, stays up-to-date on statistical/ML/AI methods research. Writes internal methodology white papers as needed.
Model validation: Performs independent validations of models prior to implementation. Validates results and business impact post-implementation.
Stays connected with the Governmental Affairs office and stay informed on current and pending federal and state legislation regarding data and model.
Actively participates in the Analytics community by giving presentations
Experience with statistical modelling using large and complex datasets, including substantial experience with (sensitive) consumer data in insurance and/or financial services.
Experience of direct people management
Experience in statistical model governance in consumer financial services or insurance.
Expertise in statistical modelling techniques such as linear regression, logistic regression, survival analysis, GLM, GBM, neural nets, cluster analysis, principal components, feature creation, and validation. Experience with comparing methodologies.
experience with several of the following: SAS (STAT, macros, EM), R, H2O, Python, SPARK, SQL, Hadoop.
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