gem
Machine Learning Engineer II
Capital Group
Seattle, USA
Full-time
Mid Level
Hybrid
USD 159354-254966 yearly
Discovered 1 weeks ago
Free
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- Design, research, build, deliver and operate machine learning systems, data pipelines, and financial models that demonstrably improve the efficiency and effectiveness of CG's investment decision making process.
- Research, train, evaluate and deploy models for financial modeling and data analysis from inception through deployment and operation.
- Uphold a high standard of quality in your work, ensuring integrity in both form and function.
- Write clear, efficient, and performant code.
- Collaborate effectively to support team strategy, contributing to decisions on modeling and technology.
- Proactively identify and tackle the root causes of endemic modeling problems, working with cross-functional teams to implement sustainable solutions.
- Work with a sense of urgency, designing and building simple, pragmatic solutions to complex problems.
- 3+ years of experience with Python and SQL, with strong object-oriented or functional design skills and understanding of common design patterns.
- A demonstrated track record in one or more ML subfields relevant to financial modeling, such as time series analysis, quantitative modeling, optimization, anomaly detection, or predictive analytics.
- Strong quantitative finance and backtesting skills, or extreme high-performance, large-scale computing experience. Candidates with both are ideal; if only one, quant finance and backtesting is strongly preferred.
- Experience solving "full stack" machine learning problems, from data collection and ETL development through model training and deployment, with a track record of using ML to solve real business problems in finance or investment management.
- Strong communication skills, with the ability to establish and maintain close working relationships with distributed team members and business partners.
- Strong computer science fundamentals including data structures, algorithms, and complexity analysis.
- Knowledge of software engineering best practices (e.g. Agile development, test-driven development, unit testing, code reviews, design documentation).
- A track record of successfully delivering enterprise grade models into production.
- Experience with AWS services (S3, containers, ECS and EKS, Lambda) preferred.
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