Lead Data Scientist (Graph Analytics)

Mastercard is hiring a Lead Data Scientist (Graph Analytics) in Toronto, Canada. Level rates it ; you can apply on Mastercard.

Level's read on this job

On Level's scale, this data scientist role is Builds AI ●●●●: the job is building AI systems.
The posting mentions Databricks, Python and AI evaluation among its tools and skills.
Level classes it as a lead role.
Mastercard has 1,000+ other open roles on Level.

AI in this role

Lead advanced machine learning and graph analytics solutions for cybersecurity and fraud detection while mentoring junior team members.

databrickspython

ai-evaluationmachine-learninggraph-analyticscybersecuritydata-science

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: 

Services within Mastercard is responsible for acquiring, engaging, and retaining customers by managing fraud and risk, enhancing cybersecurity, and improving the digital payments experience. We provide value-added services and leverage expertise, data-driven insights, and execution.

Role:

We are seeking a highly skilled and motivated Lead Data Scientist to join our Cyber Analytics team within the Security Solutions Data Science organization. The successful candidate will play a leadership role in developing advanced analytics and machine learning solutions across cybersecurity, payments, and fraud. This person will also have formal people-management responsibilities for junior team members, helping them grow their technical capabilities, manage priorities, and deliver high-quality work.

Job Responsibilities:

• Lead the design, development, validation, and implementation of advanced analytical solutions, predictive models, and machine learning algorithms using large-scale payment, fraud, merchant, cybersecurity, and threat-intelligence data.
• Provide technical leadership for graph analytics and modeling, including graph schema design, relationship modeling, community detection, link analysis, graph embeddings, and graph neural networks – all in service of addressing real-world cybersecurity problems such as breaches, payment card testing, and scam rings.
• Develop models and analytical methods that identify complex or indirect risk patterns across interconnected entities such as payment cards, merchants, domains, service providers, fraud events, digital skimmers, technologies, vulnerabilities, and threat-intelligence indicators
• Apply graph algorithms and machine learning techniques to identify hidden communities, shared infrastructure, anomalous relationships, compromise paths, and patterns that cannot be sufficiently captured through transaction-level or entity-level models alone.
• Work hands-on with Python, SQL, Spark, Hadoop, and Databricks to develop scalable data-processing pipelines, reusable analytical datasets, model features, and production-ready solutions.
• Define appropriate performance metrics, validation strategies, monitoring requirements, and business-outcome measures for machine learning solutions.
• Translate ambiguous business, product, cybersecurity, and fraud problems into clearly defined analytical approaches, technical requirements, and delivery plans.
• Partner closely with product, data engineering, software engineering, cybersecurity, operations, and other data science teams to move analytical capabilities from research through production deployment.
• Communicate analytical findings, model performance, technical tradeoffs, risks, and business implications clearly to technical and non-technical stakeholders, including senior leadership.
• Provide regular coaching, technical guidance, career-development support, and actionable feedback to junior team members.

All About You:

• Bachelor’s or Master’s degree in Data Science, Computer Science, Cybersecurity, Information Technology, Engineering, Mathematics, Statistics, Physics, or a related quantitative field.
• You are fundamentally a problem-solver who is comfortable entering complex and evolving problem spaces, identifying the most important questions, and developing practical analytical solutions.
• You have proven experience leading data science projects from problem definition and exploratory research through validation, implementation, and production handoff.
• You can translate complex technical results into clear business insights and communicate effectively and persuasively with both technical and non-technical audiences.
• You are a strong collaborator who can work across data science, product, engineering, cybersecurity, and operations teams.
• You are organized, accountable, and comfortable managing multiple priorities in an environment where analytical requirements and technical architecture continue to evolve.
• You are confident working independently while maintaining the humility to seek additional expertise, consider alternative approaches, and incorporate feedback.

Technical Skills:

• The candidate must bring substantial hands-on knowledge of graph analytics. This includes designing graph data models, engineering node and edge features, and developing graph-based machine learning solutions at scale.
• You understand how to determine when a graph-based approach provides meaningful value over conventional relational, tabular, or transaction-level modeling.
• Experience with graph databases or graph query languages is valuable, but the ability to implement scalable graph analytics outside a dedicated graph database is particularly relevant to this role.
• Advanced proficiency in Python and SQL
• Substantial hands-on experience with Apache Spark, Hadoop, and Databricks, including processing and analyzing large, distributed datasets.
• Strong knowledge of statistical modeling, supervised and unsupervised machine learning, anomaly detection, feature engineering, model evaluation, and model-performance monitoring.
• Knowledge of web application security, common cyber vulnerabilities, threat vectors, attack techniques, payment-card fraud, and methods used to compromise or monetize payment credentials is helpful.

Mastercard is a merit-based, inclusive, equal opportunity employer that considers applicants without regard to gender, gender identity, sexual orientation, race, ethnicity, disabled or veteran status, or any other characteristic protected by law. We hire the most qualified candidate for the role. In the US or Canada, if you require accommodations or assistance to complete the online application process or during the recruitment process, please contact reasonable_accommodation@mastercard.com and identify the type of accommodation or assistance you are requesting. Do not include any medical or health information in this email. The Reasonable Accommodations team will respond to your email promptly.

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.

In line with Mastercard’s total compensation philosophy and assuming that the job will be performed in Canada, the successful candidate will be offered a competitive pay based on location, experience and other qualifications for the role and may be eligible to participate in a discretionary annual incentive program. This posting reflects one or more current openings on our team.

Pay Ranges

Toronto, Canada: $127,000 - $203,000 CAD

How we rate this

Lead Data Scientist (Graph Analytics) at Mastercard rates 90 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. Read the description and count the AI tools, models and workflows it names.

Key facts

Lead Data Scientist (Graph Analytics) is an open job at Mastercard.
Level rates this role AI Level 4 of 4: Builds AI.
The role is based in Toronto, Canada.
The job was posted on October 15, 2026.
Seniority: lead.
Applications are handled by Mastercard. Apply on Mastercard

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

AI EvaluationMachine learningGraph AnalyticsCybersecurityData ScienceDatabricksPython

Also common in data scientist jobs at this level

LLM Generative AI AWS Deep learning PyTorch NLP

Pay

Typical data scientist job: $166k

This posting doesn't list pay. Use the typical data scientist figure when pay comes up. See data scientist jobs

Questions you could be asked

How do you decide that one model's output is better than another's for a given task?
Tell me about a project where machine learning was part of your work. What did you do?
Tell me about a project where graph analytics was part of your work. What did you do?
Tell me about a project where cybersecurity was part of your work. What did you do?
Tell me about a project where data science was part of your work. What did you do?

Adapt your resume

List these exact terms on your resume: AI Evaluation, Machine learning, Graph Analytics, Cybersecurity, and Data Science. 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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