Title and Summary

Manager- Data Science

Who is Mastercard?
Mastercard is a global technology company in the payments industry. Our mission is to connect and power an inclusive, digital economy that benefits everyone, everywhere by making transactions safe, simple, smart, and accessible. Using secure data and networks, partnerships, and passion, our innovations and solutions help individuals, financial institutions, governments, and businesses realize their greatest potential.
Our decency quotient, or DQ, drives our culture and everything we do inside and outside of our company. With connections across more than 210 countries and territories, we are building a sustainable world that unlocks priceless possibilities for all.

Our Team:
As consumer preference for digital payments continues to grow, ensuring a seamless and secure consumer experience is top of mind. Optimization Solutions team focuses on tracking of digital performance across all products and regions, understanding the factors influencing performance and the broader industry landscape. This includes delivering data-driven insights and business recommendations, engaging directly with key external stakeholders on implementing optimization solutions (new and existing), and partnering across the organization to drive alignment and ensure action is taken.
• Are you excited about Data Assets and the value they bring to an organization?
• Are you an evangelist for data-driven decision-making?
• Are you motivated to be part of a team that builds large-scale Analytical Capabilities supporting end users across 6 continents?
• Do you want to be the go-to resource for data science & analytics in the company?

The Role:

• Work closely with global optimization solutions team to architect, develop, and maintain advanced reporting and data visualization capabilities on large volumes of data to support data insights and analytical needs across products, markets, and services
• The candidate for this position will focus on Building solutions using Machine Learning and creating actionable insights to support product optimization and sales enablement.
• Prototype new algorithms, experiment, evaluate and deliver actionable insights.
• Drive the evolution of products with an impact focused on data science and engineering.
• Designing machine learning systems and self-running artificial intelligence (AI) software to automate predictive models.
• Perform data ingestion, aggregation, and processing on high volume and high dimensionality data to drive and enable data unification and produce relevant insights.
• Continuously innovate and determine new approaches, tools, techniques & technologies to solve business problems and generate business insights & recommendations.
• Apply knowledge of metrics, measurements, and benchmarking to complex and demanding solutions.

Role
• Ensure that all AI solutions follow industry standards for data management and privacy, covering aspects like data collection, use, storage, access, retention, output, reporting, and quality at Mastercard.
• Take a practical approach to AI, simplifying complex technical requirements to align with stakeholders' needs.
• Collaborate with global stakeholders to gather necessary information and define business problems.
• Think creatively to link AI methodologies with real business challenges.
• Identify common use case patterns to promote scalable AI through reusable models and a microservice approach.
• Build AI/ML solutions using the latest advancements from industry and academia.
• Use both open and proprietary technologies to address business problems.
• Work effectively across teams and geographies, leveraging broader team expertise to achieve AI goals.
• Partner with technical teams to deploy solutions in production environments.
• Foster a culture of learning and continuous improvement in AI capabilities.
All about you:
• 8+ years in Data Science, with a focus on AI strategy, execution, and solution development from the ground up.
• A passion for AI, demonstrated through participation in competitions such as Kaggle.
• Preferred experience or familiarity with cybersecurity, fraud, and risk solutions, including:
• Deep learning algorithms, open-source tools, and statistical environments (Python, R, SQL).
• Big data platforms like Hadoop, Hive, Spark, and GPU clusters for deep learning.
• Classical machine learning techniques such as Logistic Regression, Decision Trees, K-Means, PCA, and Time Series models (ARIMA/ARMA).
• Deep learning techniques including Random Forest, GBM, Neural Networks (CNNs, LSTMs), and optimization techniques (Adam, Adagrad, etc.).
• Frameworks like TensorFlow, Keras, PyTorch, and XGBoost for production systems.
• Experience or exposure to collaboration tools like Confluence, Bitbucket, ALM, etc.
• Knowledge of the payments industry and experience with SAFe (Scaled Agile Framework) is a plus.
Effectiveness
• Ability to manage assumptions and validate them with key stakeholders under tight deadlines, while keeping development on track.
• Strong problem-solving skills, with the ability to break down complex issues and apply the right AI techniques to solve them.
• Deep attention to detail, ensuring high confidence in developed solutions.
• Strong understanding of technical system architecture and interdependencies, anticipating challenges and proactively solving them.
Core Capabilities
• Excellent written and verbal communication skills.
• Strong project management experience.
• Background in Computer Science.

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.