Responsible for conducting research on the investment sectors of the firm and able to communicate cross-functionally with stakeholders to build, maintain, and scale the firm's insights through curating bespoke models (ranging from hedonic, regression models up to advanced machine-learning models)
Develop, Build and Maintain systematic, automated machine learning models to project localized economic trends into actionable products.
Collaborate with internal stakeholders and portfolio companies to make sure models meet business needs, are actively monitored, and are scalable
Ability to interpret complex economic problems and break down research projects into structured machine learning research projects.
See through the end-to-end deployment of machine learning solutions, incorporating some back-end data engineering workflows via cloud-based solutions (prototype to production deployment)
Drive continuous improvement and best practices for the team's data quality and ultimately model performance
Qualifications
Strong academic background in a relevant field (Computer Science, Engineering)
4+ Years of professional experience in an environment prioritizing accuracy and quality of work
Highly proficient of a scientific computing language (Matlab, Python, Perl, R, etc.) and expert with common software/database development flows and tools (Linux, git, AWS/Azure, SQL (Snowflake), NoSQL (MongoDB), etc.)
Strong expertise in AI/ML platform engineering, modern data platforms and data structures interfacing within cloud-based environments
Experience within AI/ML pipelines, MLOps and production ML/Deep Learning models (exposure across training, evaluating and deploying solutions)
Demonstrated workflow and time management skills capable of advancing multiple projects with varying timelines
Open to critical thinkers inside and outside of the financial industry, searching for passionate individuals interested in disrupting a legacy sector
Judgement - ability to work decisively under time pressure from time to time