Lead Data Scientist, NBM Program Performance

Mastercard is hiring a Lead Data Scientist, NBM Program Performance in Lisbon, Portugal. Level rates it ; you can apply on Mastercard.

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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, NBM Program Performance

Overview

The Integrity & Standards team is part of the Security Solutions organization and is responsible for securing Mastercard transactions by managing minimum security standards for stakeholders, industry standards, fraud attacks and insights, and network integrity.

The Lead Data Scientist, NBM Program Performance will report to the Vice President, NBM Governance & Performance and serve as the analytical engine for the global Network Behavior Management (NBM) program. The role will develop and maintain the measurement framework, dashboards, and recurring insights needed to evaluate the performance of existing Transaction Processing Excellence (TPE) fees, understand the ecosystem harms they are designed to address, and quantify the benefits delivered to issuers, acquirers, merchants, cardholders, and Mastercard.

This individual will translate complex network, operational, fraud, dispute, and financial data into clear decision support for NBM governance, program prioritization, stakeholder engagement, and executive communications.

Role

Develop and maintain a clear, repeatable NBM program analytics framework that measures ecosystem harm, intervention effectiveness, stakeholder outcomes, and overall program performance.

Build, maintain, and continuously improve dashboards that provide transparent views of existing TPE performance, including trends, concentration of behaviors, affected markets and stakeholders, and changes following implementation.

Define practical outcome measures and baselines for each TPE, distinguishing activity and fee collection from actual reduction of harmful behavior and improvement in ecosystem outcomes.

Assess and quantify the nature, prevalence, severity, and distribution of the ecosystem harms that individual TPEs are intended to address.

Quantify the benefits and potential unintended impacts of NBM interventions for issuers, acquirers, merchants, cardholders, and Mastercard, using a consistent methodology wherever possible.

Produce monthly, quarterly, and annual program analytics and executive-ready insights, ensuring that performance is measured consistently across TPEs, regions, and stakeholder groups.

Identify underperforming, duplicative, outdated, or data-constrained TPEs and provide evidence-based analysis to support decisions to maintain, modify, consolidate, or retire them.

Partner across Mastercard to identify authoritative data sources, validate assumptions, and improve data quality and comparability.

Create reusable analytical models and documentation, including metric definitions, calculation logic, data lineage, limitations, and controls, so that results can be understood, reproduced, and governed.

Perform root-cause, trend, segmentation, and scenario analysis to explain changes in program performance and distinguish correlation from credible evidence of program impact.

Support the evaluation of proposed new TPEs by defining the harm baseline, expected outcomes, stakeholder impacts, measurement approach, and post-implementation review requirements before launch.

Develop concise narratives and visualizations that translate technical findings into clear implications, choices, and recommended actions for governance forums and senior leaders.

Monitor the reliability and timeliness of recurring dashboards and reporting; proactively identify data gaps, anomalies, and methodological limitations and drive resolution with accountable partners.

Contribute analytical support to the NBM Revenue Transformation Steering Committee, including mapping targeted behaviors to Mastercard capabilities and providing evidence relevant to sustainable exit strategies for NBM fees.

Help strengthen a culture of objective, outcome-based performance management across the NBM program.

Key Deliverables

A governed NBM performance measurement framework with standard metric definitions and calculation methodologies.

An integrated dashboard suite covering TPE performance, ecosystem harm, stakeholder benefits, geographic and portfolio trends, and data-quality indicators.

Monthly operating insights, quarterly performance reviews, and an annual NBM program impact assessment.

TPE-level scorecards that connect the targeted behavior, measured harm, intervention, observed outcome, stakeholder benefit, limitations, and recommended action.

Pre-launch measurement plans and post-implementation effectiveness reviews for new or materially changed TPEs.

A prioritized inventory of analytical gaps and the data, ownership, or process changes required to close them.

Measures of Success

Governance forums and senior leaders have a reliable, consistent view of whether NBM interventions are reducing harmful behavior and creating measurable ecosystem value.

TPE decisions are supported by defined baselines, transparent methodologies, and evidence rather than fee or activity measures alone.

Dashboards and recurring reporting are accurate, timely, decision-oriented, and used to identify actions, owners, and priorities.

Stakeholder benefits and trade-offs are articulated across issuers, acquirers, merchants, cardholders, and Mastercard.

Data limitations are visible and progressively reduced through stronger definitions, lineage, controls, and cross-functional ownership.

NBM performance measurement is consistent across regions while preserving necessary visibility into local differences.

All About You

Significant experience in analytics, business intelligence, program performance, payments, fraud, risk, network operations, or a related field.

Demonstrated ability to build and maintain executive-level dashboards and translate complex datasets into clear business insights and decisions.

Strong quantitative skills, including metric design, trend and segmentation analysis, benefit quantification, and assessment of analytical limitations.

Experience with business-intelligence and data-analysis tools such as Power BI, Tableau, SQL, Python, or equivalent platforms.

Ability to connect operational and financial measures with customer, consumer, risk, and ecosystem outcomes.

Strong understanding of data governance, including metric definitions, data lineage, quality controls, documentation, and reproducibility.

Ability to work across global and regional teams, influence without direct authority, and build productive relationships with technical and non-technical stakeholders.

Excellent written and verbal communication skills, with the ability to create concise executive narratives and explain analytical findings to varied audiences.

High intellectual curiosity, sound judgment, attention to detail, and a willingness to challenge assumptions constructively.

Ability to manage multiple priorities independently and deliver high-quality work in a complex, evolving environment.

Experience in the payments ecosystem and knowledge of issuer, acquirer, merchant, cardholder, fraud, dispute, or network dynamics is strongly preferred.

Bachelor’s degree in analytics, economics, finance, statistics, computer science, engineering, business, or a related discipline; advanced degree or relevant professional experience is a plus.

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, NBM Program Performance at Mastercard rates 37 out of 100 for how much of the daily work is AI. That makes it Little AI (AI Level 1 of 4). The level is about AI in the job, not seniority.

Classification

Little AI. AI is not part of the work.

●●●● 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. Read the description and count the AI tools, models and workflows it names.