Quantitative Developer - Commodities

Millennium is a top tier global hedge fund with a strong commitment to leveraging innovations in technology and data science to solve complex problems for the business. Assembling a strong Quant Technology team to build the next generation in-house analytics and trader support tools, this team will sit under the Fixed Income & Commodities Technology (FICT) group. The team will be responsible for developing and maintaining the in-house pricing libraries to support trading in Fixed Income, Commodities, Credit, and FX business at Millennium. FICT provides a dynamic and fast-paced environment with excellent growth opportunities. You will be tasked with working closely with Quant Researchers, Portfolio Managers & Technology teams to build commodities fundamental analytics and modelling platform from scratch. Your responsibilities will include developing scalable libraries and APIs for commodities fundamental modelling across multiple assets and geographies. You will also be involved in building, helping maintain, and operating end-to-end modelling pipelines that involve diverse and large sets of statistical and machine learning models. Additionally, you will contribute to building scalable tools to aid data analysis and visualization, and collaborate with the broader team to develop robust delivery and operating models that enable rapid development and scalable deployment of new capabilities. To excel in this role, you must possess a strong Python fundamentals knowledge (for data science) and SQL/Database experience. You should have solid experience with all stages of the data modelling pipeline, including data access, transformation, model training and inference pipeline, and data analysis tools. The ability to work independently with hands-on experience on the complete software development lifecycle and relevant development tools is essential. Previous experience as a Quant Developer/Data Scientist in the financial industry is required, with commodities and hands-on statistical/data modelling experience being preferred. Familiarity with tools and libraries like Airflow, Flask, Dash, etc. is also preferred.,