Job
Description
As a quant engineer at DTCC, you will play a crucial role in developing and implementing quantitative models and algorithms that are essential for informed decision-making and risk management in the financial markets. Your work will involve ensuring the efficiency, reliability, and security of financial systems by adapting rapidly to changing market conditions and leveraging new data insights to solve complex business problems. **Your Primary Responsibilities:** - Extracting, cleaning, and transforming financial data from various sources. - Developing data pipelines to facilitate efficient data acquisition and processing. - Conducting statistical analysis and utilizing machine learning techniques to derive insights from data. - Creating and deploying quantitative models and algorithms using programming languages such as Python. - Translating theoretical models from quantitative researchers into practical, executable code. - Establishing and managing the infrastructure to support model development, including APIs and data pipelines. - Performing comprehensive back testing and validation of models to ensure accuracy and reliability. - Optimizing model performance for speed, efficiency, and adaptability to evolving market conditions and new data insights. - Collaborating closely with quantitative analysts and other IT teams to understand requirements and integrate models into IT systems. - Effectively communicating technical information and model performance results to various stakeholders. **Qualifications:** - Minimum of 2 years of related experience - Bachelor's degree preferred or equivalent experience - Strong proficiency in programming languages like Python, R In order to succeed in this role, you will need the following talents: - Data analytical skills to analyze large datasets, identify patterns, trends, and anomalies for model development and decision-making. - Understanding of capital markets, financial instruments, and financial risk. - Proficiency in statistical modeling, machine learning, and mathematical finance. - Strong communication and collaboration skills to work effectively with diverse teams and convey findings and model behavior to both technical and non-technical stakeholders. As a quant engineer at DTCC, you will play a crucial role in developing and implementing quantitative models and algorithms that are essential for informed decision-making and risk management in the financial markets. Your work will involve ensuring the efficiency, reliability, and security of financial systems by adapting rapidly to changing market conditions and leveraging new data insights to solve complex business problems. **Your Primary Responsibilities:** - Extracting, cleaning, and transforming financial data from various sources. - Developing data pipelines to facilitate efficient data acquisition and processing. - Conducting statistical analysis and utilizing machine learning techniques to derive insights from data. - Creating and deploying quantitative models and algorithms using programming languages such as Python. - Translating theoretical models from quantitative researchers into practical, executable code. - Establishing and managing the infrastructure to support model development, including APIs and data pipelines. - Performing comprehensive back testing and validation of models to ensure accuracy and reliability. - Optimizing model performance for speed, efficiency, and adaptability to evolving market conditions and new data insights. - Collaborating closely with quantitative analysts and other IT teams to understand requirements and integrate models into IT systems. - Effectively communicating technical information and model performance results to various stakeholders. **Qualifications:** - Minimum of 2 years of related experience - Bachelor's degree preferred or equivalent experience - Strong proficiency in programming languages like Python, R In order to succeed in this role, you will need the following talents: - Data analytical skills to analyze large datasets, identify patterns, trends, and anomalies for model development and decision-making. - Understanding of capital markets, financial instruments, and financial risk. - Proficiency in statistical modeling, machine learning, and mathematical finance. - Strong communication and collaboration skills to work effectively with diverse teams and convey findings and model behavior to both technical and non-technical stakeholders.
