Principal Data Scientist

PenFed Credit Union

McLean (VA)

Hybrid

USD 150,000 - 210,000

Full time

3 days ago
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Job summary

PenFed Credit Union is seeking a Hybrid Principal Data Scientist in Tysons, VA to lead development and monitoring of credit risk models supporting loan origination, pricing, and portfolio management.

The role requires a Master’s in a quantitative field, with PhD preferred, and 8+ years of modeling experience (6+ for PhD). Strong skills in regression, ML, survival analysis, Python, SQL, and R are expected.

Qualifications

  • Master's degree in quantitative discipline required.
  • PhD highly preferred.
  • Minimum eight years of related work experience in building statistical models and analysis.

Responsibilities

  • Independently develop statistical models and scorecards for credit underwriting, loan pricing, collection, and portfolio risk monitoring.
  • Collaborate with business partners to translate business problems into statistical problems and apply machine learning solutions.
  • Lead ad hoc analyses and deep dives to understand business drivers of trends.
  • Monitor model performance and support validation activities per governance policy.
  • Identify, measure, and monitor credit risk for M&A, loan purchases, and new strategies.
  • Improve risk identification and measurement in credit modeling activities.
  • Maintain an efficient infrastructure for model development, documentation, implementation and validation.
  • Promote innovative ways to visualize and digest complex data.
  • Collaborate across teams to drive organizational efficiency.

Skills

Logistic Regression
Linear Regression
Machine Learning
Survival Analysis
Python
SQL
R
Project Management
Communication
Data Visualization

Education

Master's Degree in quantitative discipline
PhD preferred

Tools

SQL
Python
R

Job description

PenFed is hiring a (Hybrid) Principal Data Scientist at our Tysons, Virginia location. The primary purpose of this job is to oversee the development and monitoring of credit risk models for loan originations, risk-adjusted pricing, and portfolio management. The incumbent will lead key model development initiatives, and coordinate with business partners to align modeling solutions with organizational objectives.

Equivalent combination of education and experience is considered.

  • Master’s Degree in quantitative discipline is required. PHD is highly preferred.
  • Minimum of eight (8) years of related work experience in building statistical models and advanced data analysis. Minimum of six (6) years of experience for PhD degree holders.
  • Applied experience with Logistic Regression, Linear Regression, Machine Leaning, and Survival Analysis required.
  • Advanced programming skills to include knowledge of statistical programs (e.g. SQL, Python, and R) required.
  • Experience in building risk, revenue and targeting models for consumer products is required.
  • Ability to manage multiple projects simultaneously and implement rapid changes in project direction.
  • Demonstrate strong data analysis skills, ability to understand underlying data and complex loss/balance forecasting models, various product features, possess organizational and prioritization skills, as well as strong attention to detail.
  • Detail oriented, results driven, and the ability to navigate in a quickly changing and high demand environment to develop solutions while balancing multiple priorities.
  • Critical thinking using both analytical and tactical approach to problem solving within the Quantitative Modeling team is required.
  • Ability to interact effectively with team members across the management team, Lines of Business (LOB), credit officers, finance, model governance, oversight, validation, and audit organizations.
  • Proven project management skills.
  • Excellent oral and written communication skills required.
  • Experience with A.I. tools preferred.
Supervisory Responsibility

This position will not supervise employees.

Licenses and Certifications

There are no additional certifications required.

Work Environment

While performing the duties of this job, the employee is regularly exposed to an indoor office setting with moderate noise.

*Most roles require working in an office setting with moderate noise and the ability to lift 25 pounds.*

Travel

Ability to travel to various worksites and be on-call is not required.

Pay TransparencyThe anticipated starting salary range for this role is $184.456.00 - $97,400.00This position is eligible for an organizational performance based annual bonus, subject to board discretion and approval.This position is eligible for an individual performance based annual bonus.

#LI-Hybrid

Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions. This is not intended to be an all-inclusive list of job duties and the position will perform other duties as assigned.

  • Independently develop statistical models and scorecards used in credit underwriting, loan pricing, collection, and portfolio risk monitoring.
  • Work closely with business partners and be able to translate business problem to statistical problems and leverage machine learning technology to solve business problem.
  • Independently lead the effort for any ad hoc analysis and deep dive to understand the business drivers of creation trends.
  • Monitor performance of quantitative models and support independent model validation efforts in accordance with the model governance policy.
  • Participate in identification, measurement, and monitoring of credit risk as it relates to Merger and Acquisition (M&A), loan purchases and
  • participation, and other new business strategies.
  • Improve risk identification, measurements and management with respect to credit risk modeling activities.
  • Maintain an efficient and controlled infrastructure for model development, documentation, implementation, and validation.
  • Promote innovative ways to visualize and digest complex data.
  • Work collaboratively with different teams and function to drive efficiency for the organization.
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