Lead Data Scientist

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.

Title and Summary

Lead Data Scientist

#Securitysolutions
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.
As the Lead Data Scientist, Cyber Analytics, you will be responsible for driving end-to-end data initiatives that move from model creation to tangible business value. Your primary focus will be on developing machine learning models and data pipelines that help mitigate cyber risks, while also ensuring these models are effectively integrated into products that directly serve our customers. You will work across teams—data, product, engineering, and business—to ensure your models are actionable and aligned with customer needs. If you're passionate about leveraging data science to create real-world impact and value, this role is for you.
Key Responsibilities:
• Model Development & Integration: Design, develop, and deploy machine learning models to detect, assess, and mitigate cyber risks across the Mastercard ecosystem. You'll work on end-to-end data pipelines that ingest data, analyze risks, and deliver actionable insights at scale.
• Collaboration Across Teams: Lead efforts to integrate models into customer-facing products, ensuring they deliver measurable value. Coordinate with cross-functional teams—including engineering, product management, and data analysts—to ensure that your models not only work, but are scalable, efficient, and aligned with customer needs.
• Driving Business Impact: Work closely with product and business teams to ensure that models are designed with clear business objectives in mind. You will be accountable for translating data insights into product solutions that help mitigate cyber vulnerabilities and enhance security for customers.
• Customer-Focused Innovation: Contribute to product ideation by using your deep technical expertise and creativity to solve complex problems and deliver data-driven solutions that meet customer needs in innovative ways.
• Communication & Leadership: Lead efforts to communicate complex data science concepts and model outputs to both technical and non-technical stakeholders. Serve as a subject matter expert, guiding team members and promoting data-driven decision-making throughout the organization.
What You’ll Bring:
We’re looking for a highly skilled Lead Data Scientist who is not only an expert in building sophisticated machine learning models but also has a track record of taking those models through to product deployment, ultimately driving customer value. This is a great opportunity for someone who thrives in a fast-paced, collaborative environment and is passionate about delivering real-world impact through data science.
Technical Expertise
• End-to-End Data Science Experience: Proven ability to take models from research and development to full product integration. You know how to design, build, and deploy machine learning models that make a tangible impact on products and business outcomes.
• Machine Learning Proficiency: Deep expertise in supervised and unsupervised learning, reinforcement learning, and advanced modeling techniques. Strong working knowledge of tools such as Python, SQL, and R; experience with TensorFlow, PyTorch, or similar libraries is a plus.
• Data Engineering & Pipelines: Strong experience building and optimizing data pipelines that allow seamless integration of models into production environments. Familiar with large data platforms and cloud services such as AWS, GCP, Azure, and big data tools (e.g., Spark, Hadoop).
• Cybersecurity & Fraud Analytics: Familiarity with cybersecurity, fraud detection, and risk management processes is essential. An understanding of how to model and identify vulnerabilities within payment systems and digital ecosystems is highly valued.
• Visualization & Reporting: Expertise in creating compelling data stories and presenting insights clearly through dashboards, reports, and visualizations. You know how to communicate insights to both technical and business audiences.
Business Acumen & Leadership
• Value-Driven Mindset: Ability to align data science efforts with clear business goals, and take ownership of delivering value through actionable insights and customer-centric products.
• Cross-Functional Leadership: Demonstrated ability to work closely with product managers, engineers, and other teams to ensure successful product delivery. Strong collaboration and communication skills are a must.
• Innovation & Problem Solving: A creative thinker who is always looking for new ways to improve processes, optimize models, and deliver better results. You are a proactive problem solver who thrives in dynamic, fast-paced environments.
• Customer-Focused: You understand the importance of user-centric design and how to turn data insights into valuable product features. You approach data science with the end-user in mind.
Qualifications
• Education: Advanced degree (MS or PhD) in a quantitative field such as Computer Science, Data Science, Cybersecurity, Mathematics, Statistics, Engineering, or a related discipline.
• Experience: Experience in data science, machine learning, or related fields, with significant experience in building and deploying models into production environments. Experience in the payments industry, cybersecurity, or fraud analytics is highly preferred.
• Subject Matter Expertise: Proven ability to learn complex new domains quickly and effectively. You should be able to speak the language of both data science and business, balancing technical depth with a focus on real-world impact.
#AI

Mastercard is an 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. In the US or Canada, if you require accommodations or assistance to complete the online application process or during the recruitment process, please contact [email protected] 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.