Machine Learning Engineer

Arrowpoint is an Asia-focused multi-strategy hedge fund firm headquartered in Singapore, founded by former Millennium Management Asia co-CEO Jonathan Xiong. The fund launched in July 2024 with $1 billion—making it one of the largest hedge fund launches in Asia's history. Backed by prominent investors including Blackstone, the Canada Pension Plan Investment Board, and Temasek Holdings, we operate with portfolio managers across Singapore and Hong Kong, integrating diverse strategies such as Equities, Fixed Income, and Commodities.As we continue to grow, we are seeking driven individuals to join our team and contribute to our mission.

We are on the lookout for a Junior Machine Learning Engineer who will play a key role in enhancing our Machine Learning capabilities, focusing on innovative solutions for systematic strategies. This role is not just about technical expertise but also about aligning with our forward-thinking vision and being at the forefront of shaping our AI strategy.

Key Responsibilities

ML Engineering and Operations:

Develop, implement and optimize predictive models using machine learning and deep learning / AI techniques.

Deploy, maintain and monitor production models on AWS.

ETL & Data Management:

Design and develop efficient ETL pipelines to process and manage large volumes of financial data and alternative data.

Ensure data integrity and accuracy across various data sources.

Cloud Computing:

Utilize Cloud services to deploy and manage machine learning models in a scalable and reliable manner (Terraform, AWS ECS, Docker, CI/CD pipelines).

Optimize Cloud resources for performance and cost-effectiveness.

End-to-End Solution Development:

Engage in the complete lifecycle of model development, from data collection and preprocessing to model deployment and monitoring.

Explore and implement innovative techniques to solve complex financial problems.

Stay current with the latest advancements in machine learning and data science to continually improve and innovate.

Result-Oriented Approach:

Focus on delivering high-impact results.

Communicate findings and recommendations effectively to senior stakeholders.

Requirements & Qualifications:

Bachelor's or Master's degree in Computer Science, Machine Learning, AI, Statistics, Mathematics, or a related field.

1-3 years of hands-on experience in data science and machine learning (exposure to financial markets in a professional context in a plus).

Proficiency in Python and relevant libraries (e.g., scikit-learn, Polars, PyTorch).

Experience with AWS services (e.g., S3, ECS, Lambda, Athena, CloudWatch, etc.).

Strong understanding of Data platform ETL processes and tools.

Excellent problem-solving skills and a creative mindset.

Strong communication and collaboration skills.

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