Mastercard
LeadDataScientist
Neural analysis suggests this role is
optimal for Lead candidates.
“Lead Data Scientist at Mastercard. Skills: machine learning lifecycle ownership, agentic and generative AI system development, AI quality standards, AI system evaluation and monitoring, AI solution optimization, personalization initiatives, advanced AI techniques application, rigorous experimentation, statistical analysis. Own the full machine learning lifecycle: data analysis, model development, ideation, proof of concept, production deployment, monitoring, and optimization. Lead the design, de”
What You'll Achieve.
ensure reliability, consistency, business relevance, and compliance of agentic AI; optimize AI solutions across accuracy, latency, and cost; guide decision-making and validate impact through experimentation and statistical analysis
Industry & Context.
solve complex business problems
Abide by Mastercard’s security policies, Ensure the confidentiality and integrity of the information, Report any suspected information security violation or breach, Complete all periodic mandatory security trainings
What They're Looking For.
Must Have
programming skills in Python, Github, Vibe Coding, Solid background in theoretical statistics, Hands-on experience in machine learning and deep learning, Intensive experience in structured and unstructured data, Proven experience with recommendation systems and personalization, Experience in data analysis, experimentation, and visualization
What You'll Do.
Own the full machine learning lifecycle: data analysis
production deployment
and optimization of agentic and generative AI systems for production use
Define and enforce quality standards for agentic AI
Develop robust methods to evaluate
and set guardrails for non-deterministic AI systems
Optimize AI solutions across accuracy
Build and maintain self-optimized and continuously learning algorithms
Drive advanced personalization initiatives
including personalized ranking
and contextual recommendation strategies
Apply cutting-edge AI techniques to solve complex business problems
Lead rigorous experimentation and statistical analysis to guide decision-making and validate impact
Conduct ongoing research by analyzing industry trends
academic publications
and competitor approaches to drive innovation
How You'll Work.
Communication Scope
communication
Full Job Description
**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 ### Job Description • Own the full machine learning lifecycle: data analysis, model development, ideation, proof of concept, production deployment, monitoring, and optimization • Lead the design, development, evaluation, and optimization of agentic and generative AI systems for production use • Define and enforce quality standards for agentic AI, ensuring reliability, consistency, business relevance, and compliance • Develop robust methods to evaluate, monitor, and set guardrails for non-deterministic AI systems • Optimize AI solutions across accuracy, latency, and cost • Build and maintain self-optimized and continuously learning algorithms • Drive advanced personalization initiatives, including personalized ranking, and contextual recommendation strategies • Apply cutting-edge AI techniques to solve complex business problems • Lead rigorous experimentation and statistical analysis to guide decision-making and validate impact • Conduct ongoing research by analyzing industry trends, academic publications, and competitor approaches to drive innovation Requirements • Master’s/PhD in Mathematics, Statistics, Computer Science, Engineering, or a related quantitative field, or equivalent practical experience • Strong programming skills in Python, Github, Vibe Coding • Solid background in theoretical statistics • Hands-on experience in machine learning and deep learning • Intensive experience in structured and unstructured
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