Cititec Talent Limited

Python Software Developer - Front Office Risk Development Location: Toronto, Canada Our client, a leader in the financial services sector, is seeking a proficient Python Software Developer to join their Front Office risk team. This role focuses on developing critical risk management tools, including building Value-at-Risk (VaR) engines and position management systems from the ground up. This is a high-impact, mid-level position that will enable you to contribute to a range of risk-related applications and analytics in a Front Office environment. Responsibilities Design, develop, and implement advanced Python applications and tools for Front Office risk management, specifically VaR engines, risk systems, and position management tools. Collaborate closely with traders, quantitative analysts, and other key stakeholders to gather and understand business requirements, ensuring solutions align with Front Office risk management needs. Engage in the entire software development life cycle , from requirement gathering and system design to coding, testing, deployment, and ongoing maintenance. Optimize application performance and ensure system reliability for critical risk-related applications. Apply best practices in software engineering, including code reviews, version control, and automated testing to deliver efficient, maintainable, and scalable applications. Technical Skills & Qualifications Bachelor's degree in Computer Science, Engineering, or a related field. Strong Python programming skills , with a focus on performance optimization and handling large data sets. Knowledge of quantitative finance concepts like VaR, risk metrics, and portfolio analysis. Experience with Front Office environments in financial services is highly desirable. Proficiency in SQL and familiarity with relational databases (eg, PostgreSQL). Knowledge of FastAPI and Pandas for data handling and API development. Understanding of Multithreading and asyncio for high-performance computing. Cloud development experience , preferably with AWS. Familiarity with containerization technologies (Docker, Kubernetes) for deploying scalable applications. Preferred Experience Prior exposure to financial services, ideally within a Front Office risk or trading context. Understanding of trading and risk management workflows, with experience building applications used directly by trading and risk management teams.

Python Software Developer - Data Pipeline Development Location: Toronto, Canada Our client, a prominent financial services firm, is looking for a Python Software Developer to join their data engineering team. This role is focused on designing, developing, and maintaining ETL pipelines to streamline the ingestion and processing of fundamental data . This mid-level position offers the opportunity to work on impactful data infrastructure projects in a collaborative, Front Office environment. Responsibilities Develop, implement, and optimize robust data pipelines using Python to process and integrate large volumes of fundamental data. Build and maintain ETL pipelines that support various analytical and operational applications by ingesting, cleaning, and transforming data. Collaborate with cross-functional teams and key stakeholders, including data analysts, quantitative researchers, and other software developers, to ensure data pipelines align with business requirements and data quality standards. Ensure scalability and reliability of the data infrastructure to meet the demands of high-quality, Real Time data. Apply best practices in software engineering, including code reviews, version control, automated testing, and continuous integration to deliver clean, maintainable, and efficient code. Technical Skills & Qualifications Bachelor's degree in Computer Science, Engineering, Data Science, or a related field. Proficiency in Python for data processing and pipeline development, with a strong understanding of libraries like Pandas and SQLAlchemy. Experience with SQL and relational databases (eg, PostgreSQL) for data storage, manipulation, and retrieval. Familiarity with ETL processes and tools, and experience building ETL pipelines to handle high volumes of data. Knowledge of cloud development environments, preferably AWS, for handling data ingestion and storage at scale. Familiarity with containerization technologies (eg, Docker, Kubernetes) to support scalable and flexible deployment. Experience with Apache Airflow for orchestrating complex data workflows. Data transformation skills , with a solid understanding of data quality, cleaning, and validation techniques. Strong problem-solving skills and the ability to communicate technical concepts effectively within a team. Preferred Experience Prior experience working with fundamental data in the financial sector, such as corporate earnings data, macroeconomic indicators, and other non-price-based financial data. Familiarity with Front Office environments, ideally within finance, where high-quality data drives decision-making. Knowledge of data architecture best practices and experience in building scalable, maintainable data pipelines.

Quant Developer Commodities Trading New York City (NY), Houston (TX), or London (UK) Our client is a global commodities trading firm, and are seeking an experienced and hands-on Quant Developer with a strong background in building and enhancing Value-at-Risk (VaR) engines or pricing engines to join their team. The successful candidate will play a critical role in the development, implementation, and continuous improvement of their risk management and pricing systems, with a particular focus on VaR engines. Responsibilities: Build, enhance, test and maintain quantitative models specialized for the needs of trading and risk managers, including derivatives pricing and volatility marking. The primary focus is on commodities derivatives, with exposure to other products such equity and rates derivatives. Design and develop new VaR models using historical and factor-based approaches. Research other VaR models with emphasis on commodity market volatility and seasonality. Contribute to the firm's effort to calculate and aggregate raw risk metrics (greeks) from different trading systems to enhance the firm's overall risk management capabilities. Additional emphasis is on counterparty risk with projects on PFE/XVA and initial margin calculations. Improve and extend existing risk reporting tools, including risk analysis and P&L attribution. Requirements: Advanced degree in a quantitative field such as Mathematics, Statistics, Financial Engineering, or a related discipline. At least 5+ years of experience as a commodities quant or strategist or quantitative risk officer, gained in a Hedge Fund, Oil Major, Commodities Trading House or a Bank. Good knowledge of the commodities derivatives trading landscape. Proven track record in market risk, developing and implementing VaR models, with deep knowledge of the modelling approaches and their strengths/weaknesses. Ideally, the candidate will have gained exposure to commodities specifics such as seasonality. Expert knowledge of risk and understanding of the application of complex mathematical concepts related to Monte Carlo, options pricing and time series analysis. Experience working with commodities specific models is a must.

Quant Developer Commodities Trading New York City (NY), Houston (TX), or London (UK) Our client is a global commodities trading firm, and are seeking an experienced and hands-on Quant Developer with a strong background in building and enhancing Value-at-Risk (VaR) engines or pricing engines to join their team. The successful candidate will play a critical role in the development, implementation, and continuous improvement of their risk management and pricing systems, with a particular focus on VaR engines. Responsibilities: Build, enhance, test and maintain quantitative models specialized for the needs of trading and risk managers, including derivatives pricing and volatility marking. The primary focus is on commodities derivatives, with exposure to other products such equity and rates derivatives. Design and develop new VaR models using historical and factor-based approaches. Research other VaR models with emphasis on commodity market volatility and seasonality. Contribute to the firm's effort to calculate and aggregate raw risk metrics (greeks) from different trading systems to enhance the firm's overall risk management capabilities. Additional emphasis is on counterparty risk with projects on PFE/XVA and initial margin calculations. Improve and extend existing risk reporting tools, including risk analysis and P&L attribution. Requirements: Advanced degree in a quantitative field such as Mathematics, Statistics, Financial Engineering, or a related discipline. At least 5+ years of experience as a commodities quant or strategist or quantitative risk officer, gained in a Hedge Fund, Oil Major, Commodities Trading House or a Bank. Good knowledge of the commodities derivatives trading landscape. Proven track record in market risk, developing and implementing VaR models, with deep knowledge of the modelling approaches and their strengths/weaknesses. Ideally, the candidate will have gained exposure to commodities specifics such as seasonality. Expert knowledge of risk and understanding of the application of complex mathematical concepts related to Monte Carlo, options pricing and time series analysis. Experience working with commodities specific models is a must.

Quant Developer Commodities Trading New York City (NY), Houston (TX), or London (UK) Our client is a global commodities trading firm, and are seeking an experienced and hands-on Quant Developer with a strong background in building and enhancing Value-at-Risk (VaR) engines or pricing engines to join their team. The successful candidate will play a critical role in the development, implementation, and continuous improvement of their risk management and pricing systems, with a particular focus on VaR engines. Responsibilities: Build, enhance, test and maintain quantitative models specialized for the needs of trading and risk managers, including derivatives pricing and volatility marking. The primary focus is on commodities derivatives, with exposure to other products such equity and rates derivatives. Design and develop new VaR models using historical and factor-based approaches. Research other VaR models with emphasis on commodity market volatility and seasonality. Contribute to the firm's effort to calculate and aggregate raw risk metrics (greeks) from different trading systems to enhance the firm's overall risk management capabilities. Additional emphasis is on counterparty risk with projects on PFE/XVA and initial margin calculations. Improve and extend existing risk reporting tools, including risk analysis and P&L attribution. Requirements: Advanced degree in a quantitative field such as Mathematics, Statistics, Financial Engineering, or a related discipline. At least 5+ years of experience as a commodities quant or strategist or quantitative risk officer, gained in a Hedge Fund, Oil Major, Commodities Trading House or a Bank. Good knowledge of the commodities derivatives trading landscape. Proven track record in market risk, developing and implementing VaR models, with deep knowledge of the modelling approaches and their strengths/weaknesses. Ideally, the candidate will have gained exposure to commodities specifics such as seasonality. Expert knowledge of risk and understanding of the application of complex mathematical concepts related to Monte Carlo, options pricing and time series analysis. Experience working with commodities specific models is a must.