Apply advanced statistical techniques and machine learning/deep learning algorithms to large datasets to develop models that optimize financial decision-making and operational efficiency.
Analyze and interpret complex data from various sources (structured and unstructured) to identify trends, anomalies, and actionable insights.
Build, train, and validate machine learning/deep learning models, ensuring their performance meets business needs.
Continuously improve and optimize existing models by integrating new data sources and refining algorithms.
Collaborate with cross-functional teams, including managers, engineers, and other data scientists, to define and prioritize data-driven solutions.
Communicate insights and findings to stakeholders in a clear and concise manner through reports, visualizations, and presentations.
Apply cutting-edge techniques such as Multi-agent systems (MAS), GenAI models to financial use cases.
Stay updated with the latest developments in data science, AI, and financial technology.
Requirements:
Bachelor's or Master’s degree in Data Science, Statistics, Computer Science, or a related field.
2+ years of hands-on experience in data science, preferably in financial services or fintech environments.
Strong proficiency in Python and experience with machine learning frameworks (TensorFlow,PyTorch) and ML libraries.
Knowledge of SQL and experience working with large-scale databases and data warehouses.
Excellent analytical skills, with the ability to draw meaningful insights from complex data.
Strong communication skills, with the ability to explain technical concepts to non-technical audiences.
Preferred Qualifications:
Familiarity with financial data and experience in the wealth management industry.
Experience working in Amazon SageMaker and Bedrock.
Knowledge of AWS AI/ML services and proficiency in PySpark.
