AI Research Scientist

About the position

Quantix is seeking an AI Research Scientist to help develop the next generation of predictive models, market intelligence systems, and automated analytics across the digital asset ecosystem. You will work on modeling techniques, real-time datasets, and advanced AI/ML systems that strengthen decision-making, risk visibility, and market understanding for institutional clients. This role blends research, experimentation, and hands-on model development in a fast-moving, data-rich environment.

Job responsibilities

• Design and experiment with machine learning models for prediction, classification, anomaly detection, and market behavior analysis.
• Analyze large financial and on-chain datasets to uncover patterns, signals, and actionable insights.
• Build feature pipelines, data transformations, and evaluation frameworks for model performance.
• Develop adaptive and real-time intelligence systems informed by live market feeds.
• Collaborate with engineering teams to integrate models into production systems.
• Test new modeling approaches, compare architectures, and iterate based on experimental findings.
• Document methodologies, research findings, and model behavior to support internal knowledge growth.

Required Skills

• Strong skills in machine learning, deep learning, and model experimentation.
• Proficiency with Python and ML frameworks such as PyTorch, TensorFlow, or scikit-learn.
• Experience working with large datasets, especially time-series or financial data.
• Ability to design and evaluate forecasting models, predictive systems, and statistical algorithms.
• Strong understanding of data preprocessing, feature engineering, and model validation.
• Ability to interpret model outputs, identify weaknesses, and iterate toward higher performance.
• Familiarity with cloud environments, notebooks, or GPU-based workflows is a plus.
• Comfort working in research-driven, fast-paced environments with minimal supervision.

What you will work on

• Predictive analytics
• Market and liquidity forecasting
• On-chain activity analysis
• Risk detection models
• Adaptive intelligence engines
• Algorithmic research and prototyping

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