AI Scientist – Causal AI - Quantision : Job Details

We are seeking an AI Scientist with expertise in causal AI to develop advanced algorithms for data-driven decision-making solutions. You will focus on building causal inference models that not only predict outcomes but also uncover the underlying causes driving those outcomes. Your contributions will enable more explainable, actionable, and robust AI systems in dynamic environments, particularly in the financial industry.

• Develop and implement AI models with a focus on causal inference and causal machine learning for time series forecasting, portfolio optimization, and risk assessment.
• Design and conduct causal impact analysis to identify relationships in financial data, ensuring that models capture true cause-effect relationships rather than simple correlations.
• Collaborate with data scientists and engineers to integrate causal models into AI-driven decision-making systems tailored for financial markets.
• Apply cutting-edge methods in causal discovery, treatment effect estimation, and counterfactual analysis to evaluate investment strategies and model robustness.
• Perform rigorous backtesting and validation using historical financial data to assess model performance under diverse market conditions.
• Continuously fine-tune models by leveraging the latest advancements in causal AI research.
• Stay updated with the latest developments in both causal machine learning and financial modeling.

• 3+ years of proven experience as an AI Scientist, Machine Learning Engineer, or a similar role, with a focus on causal AI or machine learning.
• Strong expertise in causal inference, causal discovery, and counterfactual reasoning.
• Hands-on experience with financial datasets and building AI models for the financial industry, such as asset management, hedge funds, or banking, is highly desirable.
• Proficiency in programming languages such as Python, R, or Julia, and experience with libraries/frameworks like PyTorch, TensorFlow, or CausalML.
• Strong mathematical foundation in probability, statistics, and algorithms, with a focus on causal reasoning.
• Experience with causal impact analysis and rigorous backtesting in financial environments.
• Excellent communication and collaboration skills to work across teams, sharing insights and results clearly and effectively.
• A PhD or Master's degree in Computer Science, Statistics, Mathematics, or a related field with a focus on AI or machine learning is preferred.