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 ScientistOverview
Our Purpose
We work 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. We cultivate a culture of inclusion for all employees that respects their individual strengths, views, and experiences. We believe that our differences enable us to be a better team – one that makes better decisions, drives innovation and delivers better business results.
In Corporate Solutions, we empower our customer’s businesses to succeed through innovative, trusted payment solutions that deliver a seamless digital-first experience around the world. We are disrupting the industry by developing world-class travel and B2B solutions for our corporate clients around the globe.
As a key player in our organization’s digital transformation journey, you will play a critical role in revolutionizing the global financial ecosystem. You will have the opportunity to make data-driven decisions, build first-in-class corporate solutions, and partner with industry-leaders to build cloud-native products and solutions for our clients.
Role Overview
We are seeking a highly skilled and experienced Lead Data Scientist to drive the development and deployment of advanced AIdriven solutions at scale. This individual will lead a team of data scientists and engineers, working closely with business leaders to identify challenges, translate them into actionable AI solutions, and integrate them into business processes. The ideal candidate will have deep expertise in both data science and engineering, experience with cuttingedge GenAI models, and a proven track record in deploying complex machine learning models across multiple domains.
Key Responsibilities
1. Leadership in Data Science and Advanced Analytics
• Lead the identification of customer challenges and work with business teams to translate them into AI driven opportunities.
• Spearhead the development and deployment of AI solutions, including advanced GenAI models, to address complex business problems.
• Provide strategic oversight for data science projects, ensuring alignment with business goals and technical excellence.
• Guide the use of advanced statistical methods and machine learning algorithms to analyze data and generate insights.
• Conduct market and technology trend analysis, bringing innovative solutions and best practices to the team.
2. Data Engineering and Pipeline Architecture
• Architect and lead the development of scalable data pipelines, ETL processes, and robust data models for enterprise wide analytical and machine learning applications.
• Direct the integration of big data technologies and cloud platforms to optimize data flow and model performance.
• Collaborate with engineering teams to establish data requirements and design high quality data systems.
• Ensure high standards of data quality and consistency across the organization, enhancing both operational efficiency and model accuracy.
3. Team Leadership and Collaboration
• Mentor and lead a team of data scientists and engineers, fostering a culture of innovation and collaboration.
• Work cross functionally with product, engineering, and business teams to implement AI solutions that deliver measurable impact.
• Present complex data driven insights to stakeholders at various levels, translating technical results into actionable business recommendations.
• Advocate for AI driven decision-making and continuously drive the adoption of data science practices across the organization.
4. Deployment and Operationalization
• Lead the operationalization of machine learning models, ensuring smooth deployment, scalability, and performance monitoring in production.
• Oversee the use of CI/CD pipelines and automated deployment tools (e.g., Jenkins, ADO) to streamline model updates and maintenance.
• Ensure models are optimized for performance, scalability, and real-time operations.
Basic Qualifications
1. Educational Background: Master’s in computer science, Data Science, Statistics, Engineering, or a related field.
2. Experience:
• 10+ years of experience in data science or data engineering roles with a focus on endtoend model development, including conceptualization, design, training, and deployment.
• 5+ years of experience leading teams and managing complex projects with a strong track record in AI and machine learning.
• Expertise in Big Data technologies (e.g., Hadoop, Spark), and cloud platforms (AWS, Azure, GCP).
• Solid understanding of machine learning algorithms (regression, classification, clustering, etc.) and advanced deep learning techniques.
• Experience in NLP techniques with libraries like Hugging Face Transformers, BERT, GPT, and timeseries models.
• Experience in managing CI/CD pipelines with tools like Jenkins, GitLab CI, or Azure DevOps for model deployment.
• Proficient in version control with Git, using Docker for containerization of models and data pipelines.
• Advanced knowledge of statistical analysis techniques, A/B testing, and hypothesis testing.
• Proficiency in timeseries analysis, forecasting, and advanced data modeling.
3. Programming Skills:
• Advanced proficiency in Python & R, with hands-on experience using libraries such as PyTorch, TensorFlow, Keras, and scikitlearn.
• Strong SQL skills, with solid understanding of relational databases and ETL processes.
4. Data and Cloud Infrastructure:
• Hands-on experience with cloud-based AI services and data engineering tools on platforms like AWS, Azure, and GCP.
• Deep experience with ETL frameworks (e.g., Apache NiFi, Talend), orchestration tools (e.g., Apache Airflow), and big data processing frameworks like Apache Spark, Hadoop, Kafka.
• Experience with data warehousing solutions (e.g., Snowflake, BigQuery, Redshift) and cloud native storage services.
Preferred Qualifications
1. Technical Expertise:
• Deep knowledge of deep learning models, NLP techniques, and frameworks such as TensorFlow, PyTorch, and Keras.
• Experience in deploying and maintaining models using platforms like TensorFlow Serving, MLflow, or ONNX.
2. Professional Skills:
• Proven experience in customer facing or consultative roles, with the ability to drive project success from design to deployment.
• Strong communication skills with the ability to present complex findings to nontechnical stakeholders.
• Ability to lead strategic conversations around AI and data science across teams and departments.
3. Soft Skills:
• Strong leadership, collaboration, and mentorship capabilities, with a passion for fostering growth within the team.
• High attention to detail and a commitment to data accuracy and reliability.
• A proactive approach to learning, with the ability to adapt quickly to new technologies and industry trends.
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.
