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JOB DESCRIPTION:
Consultants for various and unanticipated worksites throughout the U.S (HQ: Chicago, IL). Independently lead client analytics projects and conversations with clients. Collaborate with internal and external cross-functional teams to develop tailored, value-added product propositions for B2B clients. Create predictive risk management and business intelligence solutions, including reject inference and peer group analysis. Conduct in-depth analytic client engagements throughout the consumer lending portfolio lifecycle, employing advanced techniques such as ensemble methods. Design and implement programs for tasks such as data extraction, segmentation, and statistical and data analysis on extensive population datasets. Work as first-point-of-contact with clients from an analytics perspective. Communicate results and findings effectively. Make presentations to clients. Technical Environment: SAS, R, Python, SQL, Data visualization; programming and data manipulation languages (Hive, Pig, Python); Hadoop, Spark, Predictive modeling using machine learning methods; R Shiny, Command Line Interface, Ab Initio; Tableau; Linux/Unix environment; AWS cloud platform; predictive risk & BI solutions (reject inference & peer group analyses); XGBoost, hypothesis testing, survival analysis, generalized linear models; markdown/rmarkdown tools; CNN, GANs for data augmentation and synthetic data generation.

POSITION REQUIREMENTS:
Master’s degree in Statistics or Applied Mathematics or Analytics or Data Science or Information or related Quantitative field plus 2 years of experience in Statistical or Data or Quantitative Analysis required. Skills required: experience in client facing role; and with analytics, R, SAS; writing SQL queries for data extraction, segmentation & statistical analysis on large population datasets; programming & data manipulation languages (Hive, Pig, Python); Tableau; Linux/Unix; AWS; creating predictive risk & BI solutions (reject inference & peer group analyses); Machine Learning (XGBoost, generalized linear models), hypothesis testing, survival analysis; CNN, GANs for data augmentation & synthetic data generation. Telecommuting permitted.

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