Our Purpose

Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, we’re helping build a sustainable economy where everyone can prosper. We support a wide range of digital payments choices, making transactions secure, simple, smart and accessible. Our technology and innovation, partnerships and networks combine to deliver a unique set of products and services that help people, businesses and governments realize their greatest potential.

Title and Summary

Lead Data Scientist:

• Lead Data Scientist
• Foundry R&D

We are looking for a Lead Data Scientist to join Mastercard Foundry R&D. You will help drive AI innovation by exploring new technologies and building scalable, high‑impact solutions. The ideal candidate is curious, hands‑on, analytical, and comfortable working in fast‑moving R&D environments.

What you'll do

• Design and build advanced AI solutions: Lead end‑to‑end development of ML and deep learning models. Work with engineers to create scalable, reliable systems aligned with project needs. Apply NLP and other AI methods to develop prototypes and evaluate new approaches.
• Innovate with generative AI: Research emerging AI techniques, especially generative models, and turn them into Po Cs, prototypes, or product features. Experiment with small and large language models and assess their suitability for business use cases.
• Collaborate across teams: Work with data scientists, engineers, product managers, and designers to identify opportunities where AI adds value. Contribute to solution design and help integrate R&D deliverables into broader product plans.
• Provide technical leadership: Mentor junior team members in data science practices, experimentation, coding standards, and prompt engineering. Support communication of team progress through internal talks and presentations.
• Support thought leadership and IP: Conduct research, publish findings, and create internal whitepapers. File invention disclosures and patents for novel ideas developed within the R&D team.

What you'll bring

• Deep AI and ML expertise: Advanced degree preferred. 8–12+ years applying ML and deep learning to real problems. Strong understanding of NLP, generative AI, and modern transformer‑based approaches. Solid mathematical foundation and familiarity with ML algorithms.
• Strong programming skills: Proficiency in Python and its data science stack. Experience with Tensor Flow or Py Torch for deep learning. Skilled in SQL and working with large datasets. Familiarity with Spark/Py Spark, Linux, and cloud environments is an advantage.
• Innovative and analytical mindset: Comfortable experimenting with new ideas, running structured tests, and iterating based on data. Able to approach problems both creatively and pragmatically.
• Leadership and collaboration skills: Experience guiding projects or teams, coordinating cross‑functional work, and mentoring others. Able to communicate technical concepts clearly to technical and non‑technical audiences.
• Documented impact: Strong communication skills through presentations, reports, or publications. Experience with patents, conference papers, or similar contributions is a plus.

Required skills

• Educational background: Bachelor’s or higher degree in a relevant field. 8–12+ years in data science or ML roles, delivering production‑grade solutions. Strong mathematical base and understanding of ML algorithms.
• Machine learning and NLP: Practical experience with supervised and unsupervised learning, neural networks, and NLP techniques. Hands‑on work with models such as classifiers, predictors, entity extraction, and language models. Experience with LLMs and generative AI tools, including prompt engineering.
• Programming and tools: Strong Python skills with libraries such as pandas, Num Py, Sci Py, and ML frameworks. Competence in code quality, version control, and writing efficient, maintainable pipelines. Strong SQL skills and familiarity with NoSQL or data lake systems. Spark or Py Spark experience is helpful.
• AI solution development: Experience building full AI pipelines, including data extraction, cleaning, feature engineering, model training, validation, and deployment. Ability to design solution architectures and use tools like FastAPI or Flask for exposing ML services. Knowledge of MLOps tools like MLflow or containerization is a plus.
• Data handling and visualization: Skilled in working with large datasets and building efficient data pipelines. Comfortable with structured and unstructured data. Experience using visualization tools or Python libraries to communicate insights.
• Soft skills: Strong analytical thinking, communication, and documentation skills. Able to work independently and as part of a team. Adaptable and quick to learn new methods. Leadership experience is beneficial.

Preferred skills

• Advanced degrees or research: Master’s or PhD in ML/AI with publications or research experience. Participation in AI competitions or contributions to open‑source projects is valued.
• Big data and MLOps: Experience with Databricks or similar platforms, Spark clusters, and modern data formats. Knowledge of ML lifecycle tools such as MLflow, Azure ML, or Kubeflow.
• Front‑end and visualization: Experience with simple UI frameworks or dashboard tools (e.g., Streamlit, Power BI) to support demos or visualizations.
• Domain knowledge: Background in payments or financial services can speed understanding of problem spaces such as fraud detection or credit scoring. Quick learners with domain adaptability are also welcome.
• Innovation and leadership: Experience leading innovation projects, filing patents, presenting at conferences, or championing new technologies.
• Awards and certifications: Recognition in AI/ML competitions or certifications such as Google ML Engineer or AWS ML Specialty are beneficial but not required.

Corporate Security Responsibility

All activities involving access to Mastercard assets, information, and networks comes with an inherent risk to the organization and, therefore, it is expected that every person working for, or on behalf of, Mastercard is responsible for information security and must:

• Abide by Mastercard’s security policies and practices;

• Ensure the confidentiality and integrity of the information being accessed;

• Report any suspected information security violation or breach, and

• Complete all periodic mandatory security trainings in accordance with Mastercard’s guidelines.