
Data Science Solutions Specialist
1 week ago
About the Company
We are a FinTech start-up developing innovative AI tools.
About the Role
You will work on cutting-edge projects involving financial data analysis, investment research, and risk modelling. You will have the opportunity to engage in multiple mini-projects or take up a focused innovation-based research project. The project experience is designed to provide practical exposure to data science in the context of asset management, trading, and financial technology. We provide problem statements, methodology and after you submit your solution to develop the solutions/models, we also showcase to you sample solution. You can use our sample solution to modify your project submission and expand further based on suggestions given in our sample solution. You can opt for your own research based data science solution to develop/model.
Responsibilities
- Conduct data cleaning, wrangling, and pre-processing for financial datasets.
- Assist investment teams in equity research, fixed income research, portfolio management, and economic analysis.
- Apply statistical techniques to financial problems such as credit risk modelling, probability of default, and value-at-risk estimation.
- Work with big data sources including financial reports, macroeconomic datasets, and alternative investment data.
- Use either one – Python, Excel or R to analyse, visualize, and model financial data.
- Participate in research projects related to quantitative trading, financial derivatives, and portfolio optimization.
Required Skills and Qualifications
- Strong problem-solving and analytical skills.
- Able to self-learn and work independently in a remote, flexible environment.
Benefits
- This internship provides a unique opportunity to gain practical experience in data science and machine learning.
- Our team will provide support and guidance throughout the internship period.
Others
- The internship duration can be 1 month, 2 months, 3 months, 4 months or 6 months.
- Timing: Self-paced.
- Type: Unpaid.
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