Senior Data Scientist

About the role

You will design and deliver production-grade AI solutions that bridge machine learning, computer vision, and large language models specifically for a high-performance fintech environment. In this position, you own the end-to-end lifecycle of intelligent features, from initial prototyping to robust deployment and monitoring. You will rapidly experiment with novel approaches and translate complex business workflows into scalable, observable, and maintainable data products. Your work will directly influence AI strategy and empower clients by strengthening the core trading technology of a globally regulated institution. You will act as a technical leader, collaborating across data engineering, software engineering, and business teams to ensure that models drive tangible business value.

Key facts

What you'll do

• Architect and evaluate data-driven algorithms for classification, detection, segmentation, regression, and anomaly detection, blending classical statistical methods with deep learning techniques while rapidly prototyping solutions to validate business impact.
• Develop and assess LLM-based and multimodal systems for document understanding, knowledge extraction, and information retrieval, including fine-tuning foundation models, constructing RAG pipelines, and extending models for domain-specific fintech applications.
• Engineer agentic AI systems that include task-oriented agents, workflow orchestrators, tool-using agents, and autonomous reasoning frameworks, carefully translating intricate business processes into reliable and maintainable production pipelines.
• Oversee the complete machine learning lifecycle, covering data collection, rigorous cleaning and preparation, model training, thorough evaluation, secure deployment, and continuous production maintenance while championing MLOps standards and reproducibility.
• Partner with data engineers, software engineers, and domain experts to shape solution architecture and seamlessly integrate AI-enabled capabilities into existing products and operational workflows.
• Implement comprehensive monitoring frameworks that evaluate AI solution performance after deployment, proactively detecting data quality issues, model drift, and performance degradation to drive ongoing improvements.
• Engage with the wider AI research community, mentor junior data scientists, contribute to internal knowledge sharing initiatives, and elevate the collective capability of the organization's AI community of practice.
• Explore and evaluate emerging methodologies in computer vision, large language models, and multimodal learning to identify opportunities that align with strategic business objectives.
• Define and apply rigorous evaluation metrics and prompt engineering techniques, ensuring structured output design and reliable behavior for LLM-based systems used in financial contexts.
• Maintain strict awareness of regulatory and compliance considerations relevant to automated decision systems, ensuring that technical implementations remain aligned with institutional risk standards.

Requirements

• Hold 5 to 10 years of hands-on experience in classification, detection, and segmentation using both classical machine learning and deep learning approaches applied to real-world, production-grade problems.
• Demonstrate a proven track record of developing, deploying, and scaling end-to-end ML pipelines within industrial or enterprise environments where reliability and performance are critical.
• Show hands-on experience building and deploying LLM applications, working with models such as GPT, Llama, Falcon, and Claude, including fine-tuning, RAG systems, domain adaptation, and multimodal extensions.
• Exhibit experience designing and implementing agentic AI systems, including task-oriented agents, workflow orchestrators, or autonomous reasoning frameworks that can be integrated into production settings.
• Possess a strong foundation in applied mathematics, probability, and statistics that underpin modern machine learning and deep learning methodologies.
• Have advanced Python programming skills with an emphasis on writing clean, maintainable, and production-ready code that can be integrated into larger systems.
• Demonstrate deep knowledge of ML algorithms and deep learning architectures, including CNNs, Transformers, Diffusion models, and Graph Neural Networks, along with their appropriate use cases.
• Show proficiency in prompt engineering, evaluation frameworks, and structured output design specifically for LLM-based systems deployed in fintech environments.
• Bring prior experience in the fintech sector as a significant advantage, understanding the nuances of trading technology and financial data.
• Hold a Bachelor's, Master's, or PhD in Computer Science, Applied Mathematics, Statistics, or a related field; strong candidates with equivalent industry experience will be considered based on demonstrated impact.

Nice to have

• Preference for candidates who have worked with the specified technical stack and methodologies in demanding, high-availability environments.

Practical notes

• Full-time engagement based in the Dubai Office.
• Candidates must meet the outlined experience and educational requirements without exception.
• Relocation support and visa sponsorship details can be discussed upon progression in the hiring process.
• The role requires adherence to strict compliance and risk management standards inherent in financial services.
• Travel requirements, if any, will be defined in collaboration with the hiring team during later stages.
• Clear availability and timely completion of necessary documentation are essential for progression.