Responsibilities

Data Analysis and Modeling: Leverage your analytical skills to identify patterns, trends, and insights in large data sets.
Model Development and Validation: Use industry standard techniques to create features, clean datasets and develop pipelines to train and serve our models (focused primarily on classification models).
Credit Risk Assessment: Evaluate the credit risk of potential borrowers using your developed models, effectively assigning credit scores or probability of default values. Think outside the box, we often use alternative data to create our scores, use common industry practices but don’t be afraid to bring on creative solutions.
Feature Engineering: Identify and create meaningful features that enhance the predictive power of your models and capture the creditworthiness of individuals.
Cross-functional Collaboration: Work with other teams to provide insights, build models and participate in technical decision making.
Monitoring and Performance Evaluation: Continuously track the performance of deployed models, assess their metrics, and refine them as necessary to keep up with changes in behavior, market conditions, or regulations.
Research and Innovation: Stay updated with the latest advancements in data science and machine learning, and experiment with new approaches to credit risk assessment.

Requirements

Background in Data Science, Statistics, Computer Science or a related field with programming knowledge. [MUST]
Background in Data Engineering is a Plus. [DESIRABLE]
AWS Services knowledge is a Plus [DESIRABLE]
1-2 years of experience in Data Science, Data/Business Analytics (with ML knowledge). [MUST]
1-2 years of experience in ML applied to financial risk [DESIRABLE]
1-2 years of Python and SQL experience [MUST]
The ideal candidate is a creative problem-solver who is passionate about diving into data, extracting insights, and turning them into actionable decisions. [MUST]