Data Scientist

This is a Contract role for 3 to 6 months.

The Role

The Risk Operations Data Science team is looking for a Data Scientist to develop advanced machine learning models, guide measurement, strategy, and data-driven decision making to support various risk and operational areas at the client.

The Data Scientist will work closely with Credit, Risk, Product, Engineering, and Operations teams to design solutions for underwriting, account and / or portfolio management, loan processing enhancement, fraud detection and prevention, and loss mitigation, etc.

These tasks involve developing complex business rules to researching and applying state of the art machine learning modeling methodologies to solve complex business problems.

This role is very rewarding as your work will have a direct and immediate impact on the business’ profitability.

What You’ll Do

Develop, implement, and continuously improve machine learning models and strategies that support various credit, risk, and operational procedures including but not limited to underwriting, account and / or portfolio management, loan processing enhancement, fraud detection and prevention, and loss mitigation, etc

Proactively identify opportunities to apply advanced machine learning approaches (e.g., NLP, Image

Recognition, Graph Mining, etc.) to solve complex business problems

Explore and leverage in-house, external, and other open-source machine learning software / algorithms

Collaborate with Model Risk Management team to demonstrate models are developed with high level rigor that satisfy Model Risk Management and Governance requirements

Work closely with the Product and Engineering teams for model deployment

Perform ongoing monitoring of the models through the construction of dashboards and KPI tracking

Present model performance and insights to Credit, Risk, and Business Unit leaders

Requirements

What You’ll Need

Bachelor’s degree in Computer Science, Statistics, Mathematics, Physics, Engineering, or quantitative field

required. Master’s degree preferred.

2 to 3 years of relevant work experience with building and implementing machine learning models

Excellent knowledge of machine learning and statistical modeling methods for supervised and unsupervised

learning. These methods include (but not limited to) regression, classification, clustering, outlier detection, novelty detection, decision trees, nearest neighbors, support vector machines, ensemble methods and boosting, neural networks, deep learning and its various applications.

Continuously follow the advancement of machine learning and artificial intelligence to update your knowledge and skills in order to solve business problems with the most efficient methodologies

Strong programming skills in Python and machine learning libraries (e.g., sklearn, lightgbm, xgboost,

pytorch, tensorflow, keras, etc.)

Strongknowledge of databases and related languages / tools such as SQL, NoSQL, Hive, etc.

Effective communication skills and ability to explain complex models in simple terms

Nice To Have

Experience in a financial organization

Experience with model documentation and delivering effective verbal and written communication

Experience in working closely with Product, Engineering, and Model Risk Management teams

Experience with AWS or GCP

Solid knowledge of leveraging graph neural networks or Gen AI to solve some practical problems

Experience with graph databases