Data Scientist

A US-based modern financial technology company on a mission to make healthcare more accessible and affordable is looking for a Data Scientist. The selected candidate will become an early member of the risk team and work directly with the COO/Head of Product to lead the development of the core underwriting model. The fintech company is developing a point-of-sale embedded lending platform that breaks down large medical expenditures into small, monthly installments, making healthcare more accessible for patients. During their Seed round, the company managed to raise more than $3 mn in funding. This role presents an excellent opportunity to work with a fast-paced data science team and build underwriting models.

Job Responsibilities:

• Take ownership of back-testing, implement new features, and train other team members
• Build and maintain alerts, dashboards, and metrics to monitor the performance of the portfolio
• Implement and scale data pipelines, new features, and algorithms that are essential to the production models
• Evaluate new data sources, present technical concepts and results in an audience-appropriate way
• Collaborate with departments and teams to define requirements for new products
• Identify, design, and implement internal process improvements by automating manual processes, optimizing data delivery, re-designing infrastructure for greater scalability, etc.
• Improve the underwriting infrastructure, think through problems, evaluate approaches, encounter and solve challenges

Job Requirements:

• Bachelor’s/Master’s degree in Engineering, Computer Science (or equivalent experience)
• At least 3-5+ years of relevant experience as a data scientist
• Strong skills with AWS and Python 
• Solid expertise in machine learning and data science frameworks like Scikit-learn, Pandas, Numpy, XGBoost, TensorFlow, MLlib
• Strong proficiency with the AWS technical stack and data infrastructure such as MySQL, Spark, Kubernetes, Docker, and Airflow
• Demonstrated ability in machine learning with experience in areas like gradient boosting, deep learning, recommendation systems, computational advertising, reinforcement learning, etc