Intern, Data Science

As a Data Science Intern, you will have the opportunity to work on real-world data science projects that directly impact business decisions. During your 10-12 weeks with us, you will focus on enhancing our credit risk prediction models, along with exploring new data sources and applying advanced machine learning techniques to improve the accuracy and interpretability of our underwriting models. Our department is responsible for developing and deploying machine learning models that support critical business functions across the organization, including Sales & Marketing, Underwriting, Collections, and Finance. These models play a vital role in optimizing decision-making, improving efficiency, and driving business growth.

What You'll Do:

Credit Risk Prediction Model Enhancement

• Develop a refined model with improved predictive power and actionable insights for underwriting decisions.
• Improve the accuracy and interpretability of our underwriting models by incorporating alternative data sources.
• Evaluate new feature sets from transactional and bank statement data.
• Test different machine learning techniques and optimize the model for better risk assessment.

Data Exploration & Preprocessing

• Work on data cleaning, feature engineering, and preparing the dataset for model development.

Model Development & Evaluation

• Build and refine machine learning models using appropriate techniques and evaluate their performance using metrics such as accuracy, precision, and recall.

Business Insights & Recommendations

• Translate model results into actionable business insights and propose ways to implement these findings.

Data Visualization & Reporting

• Create data visualizations to effectively communicate results and insights to business stakeholders.

What We Are Looking For:

• Proficient in Python (Pandas, NumPy) or R for data analysis and manipulation.
• Experience with machine learning libraries such as scikit-learn, TensorFlow, or similar. Familiarity with algorithms such as regression, classification, clustering, etc.
• Ability to create clear and informative visualizations using tools like Matplotlib, Seaborn, or Tableau.
• Strong understanding of statistical methods for model evaluation and hypothesis testing.
• Ability to think critically and solve complex problems using data-driven approaches.
• Strong written and verbal communication skills, with the ability to present technical results to non-technical stakeholders.