Data Science Intern at Indicina

About the role

• As a Data Science Intern, you will have the opportunity to gain hands-on experience in the field of credit risk assessment and contribute to real-world projects.
• You will work closely with experienced data scientists and analysts to analyze data, develop models, and help us make data-driven decisions in the credit domain.

Job Responsibilities:

• Assist in the collection, cleaning, and exploratory analysis of credit-related datasets to identify trends, patterns, and insights.
• Collaborate with data scientists to develop and fine-tune credit risk models, including machine learning and statistical models.
• Support model validation efforts by conducting performance evaluation, model testing, and validation exercises.
• Help identify and engineer relevant features from diverse data sources to enhance model accuracy and predictive power.
• Create data visualizations and dashboards to communicate results and insights effectively to stakeholders.
• Document your work, including data preprocessing steps, model development processes, and key findings, for knowledge sharing and future reference.
• Stay updated with the latest developments in data science and credit risk assessment and propose innovative solutions to enhance our credit risk modeling efforts.
• Work closely with cross-functional teams, including data engineers, business analysts, and risk management professionals, to understand business requirements and contribute to solving complex credit-related challenges.

Required experience and qualifications:

• Strong analytical and problem-solving skills.
• Proficiency in programming languages such as Python or R.
• Basic knowledge of machine learning algorithms and statistical methods.
• Familiarity with data manipulation libraries (e.g., pandas, numpy) and data visualization tools (e.g., Matplotlib, Seaborn).
• Excellent communication and teamwork skills.
• A strong desire to learn and a passion for data science and credit risk assessment.
• Relevant coursework or projects related to data analysis, machine learning, or credit risk modeling.
• Experience with data science libraries and frameworks (e.g., scikit-learn, TensorFlow, PyTorch) is a plus.
• Previous internship or project experience in data analysis, data science, or related fields.
• Knowledge of financial concepts and credit risk fundamentals.