Indicina is a venture-funded FinTech building the technology infrastructure that will power the next generation of consumer credit platforms and businesses. We genuinely believe that risk innovation can unlock the massive African consumer credit opportunity. Only 11% of Africa's population have their credit information recorded by private credit bureaus vs 17% in Emerging Asia and 79% in Latin America. This, along with other challenges puts a major brake on consumer lending in Africa.
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.
Method of Application
Signup to view application details.
Signup Now
