Description
Mastercard is seeking a highly skilled and motivated Lead Data Scientist to join its Cyber Analytics team within the Security Solutions Data Science organisation.
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.
- Develop models and analytical methods that identify complex or indirect risk patterns across interconnected entities.
- Apply graph algorithms and machine learning techniques to identify hidden communities, shared infrastructure, anomalous relationships, compromise paths, and patterns.
- 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 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 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.
- Provide regular coaching, technical guidance, career-development support, and actionable feedback to junior team members.
Requirements:
- Bachelor’s or Master’s degree in Data Science, Computer Science, Cybersecurity, Information Technology, Engineering, Mathematics, Statistics, Physics, or a related quantitative field.
- Proven experience leading data science projects from problem definition and exploratory research through validation, implementation, and production handoff.
- Strong collaborator who can work across data science, product, engineering, cybersecurity, and operations teams.
- Organized, accountable, and comfortable managing multiple priorities in an environment where analytical requirements and technical architecture continue to evolve.
- Confident working independently while maintaining the humility to seek additional expertise, consider alternative approaches, and incorporate feedback.
Technical Skills:
- Substantial hands-on knowledge of graph analytics, including designing graph data models, engineering node and edge features, and developing graph-based machine learning solutions at scale.
- Advanced proficiency in Python and SQL.
- Substantial hands-on experience with Apache Spark, Hadoop, and Databricks.
- Strong knowledge of statistical modeling, supervised and unsupervised machine learning, anomaly detection, feature engineering, model evaluation, and model-performance monitoring.
Benefits:
- Competitive pay based on location, experience, and other qualifications for the role.
- May be eligible to participate in a discretionary annual incentive program.