Lead Data Scientist

About the role

Lead Data Scientist in Mastercard Foundry R&D, based in the Dublin TechHub, driving AI solutions across payments and commerce innovations.

Key responsibilities

• Design and develop AI solutions using machine learning, deep learning and NLP techniques.
• Research and apply emerging AI frameworks, creating proofs‑of‑concept, prototypes and products.
• Leverage small and large language models (SLMs and LLMs) to build AI‑based applications.
• Define requirements for new applications, ensuring adherence to standards and best practices.
• Coach junior team members and represent the group in public speaking engagements.
• Collaborate with cross‑functional, global teams on product innovation and R&D initiatives.
• Publish research papers and file patents for AI solution concepts.
• Identify opportunities where AI can act as a key enabler.

Required profile

• Minimum 7 years of professional experience in data science.
• Strong passion for creating innovative solutions in a collaborative environment.
• Advanced knowledge of modern software engineering concepts and methodologies.
• Commitment to code quality and best coding practices.
• Proactive attitude with the ability to take on challenging projects.
• Excellent verbal and written communication, with strong teamwork skills.
• Motivated, driven and able to mentor junior data scientists.
• Proven ability to lead projects, manage timelines and deliver solutions independently or as a small team lead.

Required skills

• Python and its data‑science ecosystem (pandas, numpy, scikit‑learn, Keras, PyTorch, Transformers, spaCy, gensim).
• FastAPI and JSON for building APIs.
• Prompt engineering and experience with LLMs such as GPT, Llama, dbrx, gemma, Mistral.
• SQL query optimization, stored procedures, and AI solution architecture design.
• Streamlit for simple user interfaces.
• Version control with GitHub and Bitbucket.
• PySpark and Unix command‑line proficiency.
• Cloud platforms, primarily Azure, with exposure to Databricks and MLOps tools like MLflow.
• Front‑end frameworks (Angular or React) and dashboarding with PowerBI (optional).