Data Scientist

Kyriba is a global leader in liquidity performance that empowers CFOs, Treasurers and IT leaders to connect, protect, forecast and optimize their liquidity. As a secure and scalable SaaS solution, Kyriba brings intelligence and financial automation that enables companies and banks of all sizes to improve their financial performance and increase operational efficiency. Kyriba’s real-time data and AI-empowered tools empower its 3,000 customers worldwide to quantify exposures, project cash and liquidity, and take action to protect balance sheets, income statements and cash flows. Kyriba manages more than 3.5 billion bank transactions and $15 trillion in payments annually and gives customers complete visibility and actionability, so they can optimize and fully harness liquidity across the enterprise and outperform their business strategy. For more information, visit www.kyriba.com.

We are seeking a Mid-Level Data Scientist with a strong interest in AI agent systems to join our growing development team and help us enhance product reliability and performance. One of our team's core missions is to create innovative, scalable, and impactful AI solutions that extend the Kyriba Platform's functionality and fulfill growing customer needs and expectations.

As a Mid-Level Data Scientist, you’ll play a key role in designing, developing, and deploying agentic AI solutions that understand financial domain complexities and deliver trustworthy, explainable results. You’ll be involved in single and multi-agent design, prompt engineering, context engineering, advanced evaluation of agentic workflows, RAG (Retrieval-Augmented Generation) systems, and other agent-related topics. You'll collaborate with cross-functional teams, including software engineers, product managers, and other data scientists, to deploy and iterate on scalable AI solutions.

The perfect candidate doesn't need to fulfill all the requirements listed below—we are looking for talented colleagues who are willing to learn, motivated, and brave enough to tackle complex problem-solving challenges.

Keywords: AI, GenAI, AI Agents, LLM, LangGraph, LangChain, RAG, Python, Databricks, Mosaic AI, Vector Databases, Prompt Engineering

Essential duties and responsibilities:

• Proactively assist in the design, development, and testing of production-ready AI agent systems for financial applications
• Work closely with senior team members to implement, evaluate, and optimize AI agents using techniques such as prompt+context engineering, few-shot learning, and chain-of-thought reasoning
• Support data extraction, preparation, and feature engineering, including maintaining vector stores and embedding pipelines for RAG systems
• Contribute to the development of evaluation frameworks to assess agent performance, accuracy, hallucination detection, and response quality
• Collaborate with cross-functional teams to define agent requirements, performance metrics, success criteria, and deliverables
• Participate in code reviews, unit testing, integration testing, and documentation to ensure code quality, reproducibility, and maintainability
• Stay updated on the latest research and developments in LLMs, agentic frameworks, and foundation models.
• Research and experiment with emerging approaches to advance agent systems in terms of efficiency, accuracy, reasoning capabilities, and reliability

It's fun to work in a company where people truly BELIEVE in what they're doing! We're committed to bringing passion and customer focus to the business.

3+ years of hands-on experience in data science or applied machine learning Master's degree (or equivalent) in Computer Science, Data Science, Machine Learning, Statistics, or related field Strong programming skills in Python with knowledge of software engineering best practices (version control, testing, documentation) Hands-on experience with LLMs and modern AI frameworks, including:

Solid understanding of NLP concepts: embeddings (Word2Vec, BERT, sentence transformers), semantic search, transformers architecture, attention mechanisms, tokenization, and fine-tuning Knowledge of RAG systems: document chunking strategies, retrieval mechanisms, context window management, and hybrid search approaches Strong foundation in classical ML: supervised/unsupervised learning, evaluation metrics (precision, recall, F1, ROC-AUC), cross-validation, and hyper-parameter tuning Familiarity with data platforms like Databricks is a plus Excellent analytical, problem-solving, and communication skills. Courage to innovate and introduce AI agents to solve complex fintech challenges Collaborative team player comfortable working in an agile, cross-functional environment Attention to detail and commitment to delivering high-quality, well-documented work Intermediate (at least) English level with good verbal and written communication skills.