Data Scientist / ML Engineer
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Job Description
Data Scientist / ML Engineer developing scalable machine learning systems for Antarctica Capital. Collaborating with teams to optimize models and infrastructure in a remote setting.
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.
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.
- 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.
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