Data Scientist / ML Engineer (Antarctica Capital)

EarthDaily Analytics•Vancouver, BC

3d•$145,000 - $170,000•Remote

About The Position

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.

Requirements

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.
Core Technical Skills: Tensorflow, Pytorch, Python, Pydantic, AWS Lambda, Fargate, Step Functions, other usual suspects, IaC / CDK

Nice To Haves

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.
Additional Technical Skills (Highly Valued): API development with FastAPI

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: 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