AI Data Scientist: Build Scalable Financial ML Solutions

Point72; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

Cubist’s Data Services (CDS) group is seeking an AI Data Scientist to join our dedicated data team and deliver comprehensive data for predictive modelling in finance. We build scalable AI solutions and maintain reliable data delivery for demanding portfolio managers.

You will design and deploy AI/ML models, develop end-to-end pipelines, and collaborate with product, engineering, and business teams to translate complex problems into data-driven outcomes.

Qualifications

• PhD in Computer Science with AI/ML specialization.
• Expert Python and SQL programming.
• 3+ years as Data Scientist or similar.
• Strong publication record in top ML conferences.
• Experience with large data sets and predictive modeling.

Responsibilities

• Build AI-powered data products using ML models and LLMs.
• Design, train, fine-tune, evaluate, and deploy in-house AI models with datasets.
• Develop end-to-end model pipelines from data prep to deployment.
• Apply generative AI to summarization, classification, semantic search, and automation.
• Collaborate with product, engineering, and business teams to translate problems into scalable AI solutions.
• Evaluate third-party AI tools and internal models.
• Build data pipelines and model workflows for structured and unstructured data.
• Define model performance metrics, run experiments, and improve quality.
• Support production deployment with reliability, governance and responsible AI usage.

Skills

Python

SQL

Publications

Communication

Multitasking

Ethics

Education

PhD in Computer Science

Tools

SQL

Job description

Cubist’s Data Services (CDS) group is seeking an AI Data Scientist to join our dedicated data team and deliver comprehensive data for predictive modelling in finance. We build scalable AI solutions and maintain reliable data delivery for demanding portfolio managers.

You will design and deploy AI/ML models, develop end-to-end pipelines, and collaborate with product, engineering, and business teams to translate complex problems into data-driven outcomes.