Lead Data Scientist

AI in this role

Lead end-to-end data science projects and build innovative predictive analytics models at Mastercard.

data-sciencepredictive-modelingmachine-learningstatistical-analysisstakeholder-managementleadership

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

Overview

Mastercard is seeking a highly skilled and results-driven Lead Data Scientist to drive advanced analytics initiatives and deliver data-driven insights that address critical business challenges. This role requires strong technical expertise, stakeholder management, and leadership capabilities to translate complex business problems into scalable analytical solutions. The ideal candidate will lead end-to-end data science projects, mentor team members, and contribute to building innovative analytical products that generate measurable business value.

Role
Lead complex analytics initiatives and projects to derive actionable insights from large and diverse datasets, enabling informed business decision-making.
Partner with clients, stakeholders, and cross-functional teams to translate business needs into technical analyses, models, and data science solutions.
Present analytical findings, recommendations, and business outcomes effectively to both technical and non-technical audiences.
Identify and leverage rich data sources while overseeing data integration, transformation, and quality processes to ensure reliability and consistency.
Own the delivery of high-quality analytical solutions within established timelines and budget parameters, including conducting post-implementation reviews and continuous improvement activities.
Guide the design and development of sophisticated predictive models, analytical frameworks, dashboards, and prototypes using advanced statistical and machine learning techniques.
Review and delegate work across the team to ensure project milestones are achieved and downstream applications are not impacted.
Drive best practices in analytics, model development, and data science methodologies to improve solution effectiveness and scalability.
Serve as a technical leader and trusted advisor for complex analytical challenges across the organization.

All About You
Proven experience leading complex analytics and data science initiatives from concept through implementation.
Strong expertise in statistical modeling, machine learning, predictive analytics, and advanced data science methodologies.
Demonstrated ability to translate business requirements into scalable analytical solutions and actionable insights.
Experience working with large, complex datasets and managing data quality, integration, and transformation processes.
Strong stakeholder management and communication skills with the ability to present technical concepts to diverse audiences.
Experience developing analytical products such as predictive models, dashboards, visualization tools, and decision-support frameworks.
Ability to manage multiple priorities and deliver high-quality outcomes in a fast-paced environment.
Proven mentoring and coaching experience, with a passion for developing technical talent and fostering a collaborative team culture.
Strong problem-solving skills, intellectual curiosity, and a continuous learning mindset.
Experience working in cross-functional, global, and matrixed environments is preferred.

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.

How we rate this

Lead Data Scientist at Mastercard rates 80 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

Data SciencePredictive ModelingMachine LearningStatistical AnalysisStakeholder ManagementLeadership

Questions you could be asked

Tell me about a project where data science was part of your work. What did you do?
Tell me about a project where predictive modeling was part of your work. What did you do?
Tell me about a project where machine learning was part of your work. What did you do?
Tell me about a project where statistical analysis was part of your work. What did you do?
Tell me about a project where stakeholder management was part of your work. What did you do?

Adapt your resume

List these exact terms on your resume: Data Science, Predictive Modeling, Machine Learning, Statistical Analysis, and Stakeholder Management. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.

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