AI Data Scientist

Role

We are passionate about data. We collaborate to build elegant, effective, scalable and highly reliable solutions to empower predictive modelling in finance.

Cubist’s Data Services (CDS) group is looking for an AI Data Scientist to join our dedicated data team. Our group is responsible for the timely delivery of comprehensive and error-free data to some of the most demanding and successful systematic Portfolio Managers in the world.

Responsibilities

• Build AI-powered data products using machine learning models, large language models, and modern AI tools.
• Design, train, fine-tune, evaluate, and deploy in-house AI/ML models using proprietary and external datasets.
• Develop end-to-end model pipelines covering data preparation, feature engineering, training, validation, deployment, and monitoring.
• Apply generative AI and LLM techniques to use cases such as summarization, classification, knowledge extraction, semantic search, and workflow automation.
• Work with product, engineering, and business teams to translate business problems into scalable AI solutions.
• Evaluate and integrate third-party AI tools, APIs, open-source models, and internal models where appropriate.
• Build data pipelines and model workflows for structured and unstructured data.
• Define model performance metrics, run experiments, conduct error analysis, and continuously improve model quality.
• Support production deployment of AI models, ensuring reliability, scalability, governance, and responsible AI usage.

Requirements

• PhD in Computer Science, with a specialization in AI or machine learning related domains
• Strong programming skills in Python and SQL
• Strong publication record in top tier machine learning conferences
• 3+ years of experience as a Data Scientist or similar role
• Experience working with large data sets including predictive modeling
• Financial industry experience preferred but not required
• Strong organization, communication and interpersonal skills
• Intellectual curiosity and enthusiasm for learning
• Attention to detail and a love of processes
• Strong oral and written communication skills
• Ability to multitask and prioritize assignments
• Commitment to the highest ethical standards