Data Scientist

Role Summary As a Data Scientist at ComplyAdvantage, you will develop and refine data-driven solutions to detect and prevent financial crime globally. Your core focus is RD and Strategic Initiatives, conducting forward-looking research in ML, NLP, and Agentic AI to create measurable business value. In addition to deep-dive RD, you will provide advisory support to Product and Engineering teams. By investing in AI Enablement workstreams, you will empower these departments with the quantitative insights and frameworks necessary to scale high-impact, AI-driven features. Key Responsibilities Advance RD in GenAI and Agentic AI : Develop high-impact prototypes for financial crime detection, leveraging multi-agent systems and RAG architectures built on foundational LLM frameworks. Contribute to Core Solutions : Collaborate with Engineering and Product to integrate and optimise AI components and frameworks into existing and new product features, ensuring reliability, scalability, and ethical performance in production. Evaluate LLM-based Systems: Contribute to the development and implementation of robust evaluation methodologies for LLM and agentic AI based systems, tracking key metrics related to task completion, reasoning accuracy, tool effectiveness, and overall reliability. Evaluate Model Context Protocol (MCP) applications: Connect our AI systems with external data sources and tools and provide recommendations for engineering implementation Data Understanding: Develop a solid understanding of our existing data sources, identifying challenges and suggesting potential improvements. Data Analysis: Use exploratory data analysis and data mining techniques to identify actionable insights for product improvement, including patterns that can inform and refine agent behavior and prompt engineering strategies. Research Innovation: Continuously research and evaluate emerging tools, technologies, and best practices in data science, especially within the rapidly evolving field of Agentic AI, to keep ComplyAdvantage at the forefront. Cross-Functional Collaboration: Engage effectively with multi-disciplinary teams to actively share best practices, expertise and inspire innovation to foster an AI-first culture across the organization. Visualization Roadmapping: Provide clear data insights and visualizations that will act as a basis for new ideas to be integrated into our product roadmap. Required Qualifications MSc or PhD in a numerate subject (e.g., Computer Science, Mathematics, Statistics, Physics) or equivalent practical experience in a data-intensive role. 2-4 years of professional experience in data science or a related field, with a demonstrated ability to contribute to impactful data-driven solutions. Proficiency in Python, including experience with core data science libraries (e.g., Pandas, NumPy, Scikit-learn). Hands-on experience with modern machine learning frameworks such as TensorFlow or PyTorch. Familiarity with the foundational concepts of agentic AI and large language models (LLMs). This can be professional or through personal research. Basic understanding of popular agentic AI frameworks (e.g., LangChain, Pydantic AI). Knowledge of general evaluation techniques for AI models and an eagerness to learn and apply methods specific to agentic systems. Ability to communicate complex technical concepts effectively to a variety of stakeholders. Strong passion for data and solving challenging real-world problems using algorithms. Preferred Qualifications Prior experience contributing to the deployment and integration of machine learning models into production data pipelines. Exposure to Big Data platforms such as Spark. Experience with Natural Language Processing, Entity Resolution, Graph Theory, or Graph Machine Learning. Experience working with or evaluating multi-agent systems or complex AI workflows. What Success Looks Like In 90 days: You have successfully onboarded, gaining a clear understanding of ... (truncated, view full listing at source)