Loss Forecasting Data Scientist

Piper Companies is seeking a Data Scientist to join one of the nation’s largest premier credit unions based in Mclean, VA. The Data Scientist's primary responsibility will be CECL modeling to analyze credit and prepayment risk, determine the allowance for loan losses, and support financial planning. This position will be hybrid in Mclean, VA!

Responsibilities of the Data Scientist include:

• Implement models for loan loss allowance, CECL, stress testing, new volume origination, line of credit utilization, and prepayment models for all products, including credit card, personal loan, student loan, auto loan, mortgage, and commercial loan.
• Maintaining documentation for key processes and model components across the team with a focus on standardization of processes that satisfy model risk management, audit, and regulatory requirements.
• Implement vendor-developed models for consumer and commercial credit loss or prepayment.

Qualifications for the Data Scientist include:                                                                                            

• 2+ years of experience in quantitative modeling, development, or implementation.
• Working experience in data manipulation and advanced data analysis.
• Experience with SAS, R, Python, and proficiency working with large datasets is required.
• Must have extensive experience with Logistic Regression, Linear Regression, Survival Analysis, Time Series Analysis, Decision Trees, Cluster Analysis, Markov Chain, Machine Learning
• Must have a Masters Degree in a Quantitative Field: Statistics, Mathematics, Economics, Finance, Analytics, etc,
• Banking analytics/modeling experience is preferred: CCAR/CCEL Modeling, Fraud Detection, AML

Compensation for the Data Scientist include:

• Hourly Pay: $70 - $73/hr -- Salary Range: $140,000-$150,000
• Full benefits: PTO, Paid Holidays, Cigna Healthcare, Dental, Vision, 401k with ADPTS

Keywords: #LI-CR2 #LI-HYBRID

Data, data science, R, sas, python, datasets, logistic regression, linear regression, survival analysis, time series analysis, decision trees, cluster analysis, real estate, auto, credit card, consumer lending, consumer banking, modeling, model implementation, model risk, documentation, prepayment risk, financial services