Data Scientist / ML Engineer (Antarctica Capital)

Skills

What you'll do

• Collaborate with architect and author of neural network bond risk product to identify areas for improvement.
• Lead architecture and development effort
• Contribute to the design, development, and deployment of firm-wide architecture, norms, policies, infrastructure and methodologies for machine learning activities across multiple company groups.
• Design, develop, and deploy machine learning models into production environments.
• Collaborate with data scientists to translate prototypes into production-ready systems.
• Build and maintain data pipelines, feature stores, and model-serving infrastructure.

About the role

OPPORTUNITY We are seeking a highly skilled Data Scientist / Machine Learning Engineer to help design, build, deploy, and maintain scalable machine learning systems within Antarctica Capital as part of the Octantis platform. A key initial area of focus for this role will be deep collaboration with the architect/author of an existing neural network used to predict risk factors associated with bonds. In this capacity you will develop an understanding of the existing modeling techniques; identify opportunities for improvement across model performance, infrastructure, reliability, and cost; and lead implementation of those improvements. Beyond the initial focus area, this role will have significant opportunities to deliver impactful, value-generating capabilities within the firm and a fast, flexible, agile team on which to work. KEY RESPONSIBILITIES: Refactor Neural Network • Collaborate with architect and author of neural network bond risk product to identify areas for improvement. • Lead architecture and development effort Ongoing • Contribute to the design, development, and deployment of firm-wide architecture, norms, policies, infrastructure and methodologies for machine learning activities across multiple company groups. • Design, develop, and deploy machine learning models into production environments. • Collaborate with data scientists to translate prototypes into production-ready systems. • Build and maintain data pipelines, feature stores, and model-serving infrastructure. • Evaluate and optimize model performance, latency, and scalability. • Implement automated training, testing, and deployment workflows (MLOps). • Monitor models in production and address issues related to drift, performance degradation, or data quality. • Conduct code reviews and ensure best practices in ML engineering and software development. • Stay current with emerging ML/AI technologies and recommend tools or frameworks that improve team efficiency. Other Duties as Assigned EXPERIENCE • 7+ years building machine learning models with Python and AWS. • Hands-on experience with ML frameworks such as Pytorch and TensorFlow. • Experience with ML observability and training platforms/technologies like ML Flow. • Proficiency in building and deploying models using cloud platforms such as AWS (e.g. in Fargate) • Solid understanding of algorithms, data structures, and software engineering principles. Preferred: • Experience with data and compute orchestration tools like AWS Step Functions or Apache Airflow. • Exposure to large scale data warehousing and query engine technologies like Iceberg and Athena, and to columnar data storage formats like parquet. • Experience working with and modernizing legacy software, including migrating from on-prem to cloud-based deployments. SKILLS / KNOWLEDGE Core Technical Skills (Required): • Tensorflow, Pytorch • Python, Pydantic • AWS Lambda, Fargate, Step Functions, other usual suspects • IaC / CDK Additional Technical Skills (Highly Valued): • API development with FastAPI WORKING ENVIRONMENT • Fully remote role open to individuals located in and working from the U.S. and Canada. • Agile software development with daily standups and weekly Scrum cadence. • Fast-paced environment with need to adapt quickly to time-sensitive deliveries. • Working hours: 9:00 AM – 5:00 PM Central Time Monday through Friday (except recognized holidays); be available for a minimum of six (6) hours daily during this period to facilitate collaboration. YOUR COMPENSATION Base Salary Range: $145,000-$170,000 CAD annually. This range is based on Vancouver, BC-derived compensation for this role and may differ for other geographies. The selected candidate's compensation will be determined based on multiple factors, including but not limited to job-related skills, experience, education, and location.