Risk Data Scientist
- New York, NY
MoneyLion is looking for a Risk data scientist to work on applying modern machine learning techniques to develop and enhance our credit risk models. This entails understanding and leveraging our various internal and external data sets in consumer credit, retail financial data and more to create predictive credit decisioning models.
Build probabilistic credit models to predict outcomes based on historical data.
Train and test the various machine learning techniques on data sets to investigate credence to
hypotheses on different business challenges.
Work with engineering team members to deploy high performing candidate models to
Work with members of different business divisions to generate ideas on how to use data to
make processes more efficient or improve economics.
Formulate decisioning schedule to ensure predictions will not derail critical business metrics.
Identify, monitor, and measure model performance over time.
Develop dashboards to visualize findings in precise and meaningful ways.
Undergraduate in Math, Computer Science, Statistics, or related technical field.
Strong mathematical, statistical, and programming background.
Must have hands on experience in machine learning, predictive analytics, and statistical
Proficiency in R, Python (Jupyter, Pandas, Numpy, sklearn, etc.), and SQL.
Ability formulate novel solutions to problems, communicate thought process clearly, and think
within the context of larger business needs and targets.
Advanced degree in Math, Computer Science, Statistics, or related technical field.
2+ years of industry experience in predictive analytics and/or statistical modeling role.
Experience building machine learning models using traditional and alternative consumer credit
MoneyLion is committed to equal employment opportunities for all employees. Inside our
company, every decision we make regarding our employees is based on merit, competence,
and performance, completely free of discrimination. We are committed to building a team that
represents a variety of backgrounds, perspectives, and skills. Within that team, no one will feel
more “other” than anyone else. We realize the full promise of diversity and want you to bring
your whole self to work every single day.
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