Avp/vp, Quantitative Developer, Investment Insights Group

4 weeks ago

Singapore GIC Private Full time
GIC is a custodian of Singapore's Reserves and a Sovereign Wealth Fund to secure Singapore's financial future. GIC is entrusted with a critical mission that impacts the lives of all Singaporeans. We are looking for the best talent to help us fulfil our commitment to the future of Singapore and its people.

Investment Insights Group (IIG)
The Investment Insights Group (IIG) uses advanced quantitative techniques and cutting-edge technological tools to generate impactful insights that drive superior investment outcomes for GIC. We are a team of high performing Data Scientists, Software Developers, and asset-specific Strats that work closely with each of our investment departments - from Public Markets like Equities and Fixed Income, to Private Markets like Private Equity and Real Estate - to drive high-impact innovations to enhance cross-asset investment capability, business agility and efficiency.

We are looking for a Quantitative Developer to partner with our Quantitative Strats to transform our quantitative research and modeling engineering stack.

Responsibilities Be part of the team to design, develop and maintain our next generation event-based quantitative modeling platform for quantitative research and model productionisation Devise and implement the best-in-class DevOps to support rapid research and model productionisation demands with the necessary control and governance Handle production issue relating to our end-to-end quant operations Be a core contributor and gatekeeper to our quantitative libraries written in R and Python for scientific modelling, evaluation, back testing, as well as reporting purposes Implement best practices for coding in R and Python for the quant strats Join us if you have/are A Bachelor's or Master's degree in Engineering/Applied Mathematics/Computer Science subjects with exposure to financial markets or scientific modeling. Proficient in R and Python. Have experience building libraries supporting a group of researchers for scientific modelling, evaluation, as well as reporting purposes. Experience in designing and maintaining data processing systems for both batch and streaming demands (e.g. Apache Spark, Apache Beam, Apache Kafka, Apache Airflow) Familiar with different database, object store and caching technologies (e.g. MSSQL, Snowflake, Neo4j, S3, NoSQL, Redis) Familiar with cloud services and serverless technologies including and not limited to AWS, Docker, Kubernetes Familiar with DevOps (e.g. Jenkins, GitOps) and/or MLOps (e.g. AWS SageMaker, ML Flow)

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