Proficiency in programming languages such as Python, R, and SQL for data manipulation and model development.
Proven experience with financial modeling, risk assessment, and quantitative finance techniques.
Strong background in machine learning algorithms (e.g., regression, classification, clustering, time series forecasting) and their application in business and finance.
Excellent communication skills, with the ability to present complex financial data and analysis to non-technical audiences.
Experience with data visualization tools such as Tableau, Power BI, or Matplotlib.
Proven track record of applying data science techniques to drive financial performance and solve business problems.
Familiarity with cloud platforms (e.g., AWS, Azure, GCP) and big data tools is a plus.
Experience working with financial data, such as stock prices, credit scores, or financial forecasting.
Knowledge of time series forecasting, econometrics, and optimization techniques.
Expertise in statistical modeling, financial risk analysis, or algorithmic trading.
Experience with tools like TensorFlow, PyTorch, or scikit-learn for machine learning model development.