At Mastercard, we aim to connect and power an inclusive, digital economy that benefits everyone, everywhere. We achieve this by making transactions safe, simple, smart, and accessible. Utilizing secure data and networks, partnerships, and our passion for innovation, we help individuals, financial institutions, governments, and businesses achieve their greatest potential. Our decency quotient (DQ) drives our culture and everything we do inside and outside the company. We foster a culture of inclusion that respects individual strengths, views, and experiences, believing that our differences enable us to make better decisions, drive innovation, and deliver superior business results.
Title and Summary
Data / ML Engineer-4
Mastercard Overview
Mastercard is the global technology company behind the world’s fastest payments processing network. We facilitate commerce, connect financial systems for the previously excluded, serve as a technology innovation lab, and are home to Priceless®. We believe that as our company grows, so should our employees, connecting everyone to endless, priceless possibilities.
Join a Fast-Growing Team
As a Data / ML Engineer on the Data Engineering & Analytics team, you will develop solutions that leverage vast datasets gathered by retail stores, restaurants, banks, and other consumer-focused companies. The challenge will be to create high-performance algorithms and cutting-edge analytical techniques, including machine learning and AI, and intuitive workflows to derive insights from big data, driving businesses forward. You will develop high-performance analytic solutions based on datasets measured in billions of transactions and create front-end visualizations to unlock the value of big data. You will develop data-driven innovative analytical solutions, identify opportunities to support business and client needs quantitatively, and facilitate informed decisions through activities such as building ML models, automated data pipelines, designing data architecture/schema, and performing jobs in big data clusters using various execution engines and programming languages like Hive/Impala, Python, Spark, R, etc.
Your Role
- Drive Evolution of Data & Services: Focus on data science and engineering to enhance products and platforms.
- Transform Unstructured Data: Auto-tag images and convert text to speech.
- Solve Complex Problems: Optimize existing machine learning libraries and frameworks.
- Support Deployed Applications: Act as a trusted advisor to data scientists and consumers, identifying data problems and guiding issue resolution.
- Ensure Data Governance: Implement or validate data lineage, quality checks, and classification.
- Incorporate New Data Sources: Enhance insights and expand testing capabilities with real-time, streaming, batch, and API-based data.
- Innovate Continuously: Experiment with new tools to streamline development, testing, deployment, and running of data pipelines.
- Develop Infrastructure: Build data and analytic infrastructure for product development.
- Collaborate Across Teams: Partner with consultants, engineering, and sales to solve priority problems.
- Evaluate Analytics Solutions: Consider usability, technical feasibility, timelines, and stakeholder opinions.
- Break Down Solutions: Create smaller, releasable milestones to gather feedback.
- Evangelize Releases: Incorporate user feedback and track usage for future development.
- Ensure Proper Data Governance: Implement or validate data lineage, quality checks, and classification.
- Define Vision and Culture: Work with cross-functional teams to establish vision, culture, and processes.
- Focus on Value Drivers: Prioritize operational activities according to key drivers of organizational value.
- Escalate Technical Issues: Report technical errors or bugs detected in project work.
- Stay Updated: Maintain awareness of technical and product trends through self-learning and training.
- Support ML Production Systems: Design pipelines and engineering infrastructure for scaled machine learning production systems.
Ideal Candidate Qualifications
- Experience with Python/Scala, Spark, SQL, and Hadoop platforms.
- Proficient with data pipeline and workflow management tools like NIFI and Airflow.
- Comfortable developing shell scripts for automation.
- Proficient in version control, testing, and deployment.
- Basic knowledge of statistical analytical techniques, coding, and data engineering.
- Curiosity, creativity, and excitement for technology and innovation.
- Demonstrated quantitative and problem-solving abilities.
- Motivation, flexibility, self-direction, and ability to thrive on small project teams.
- Good communication skills and strong collaboration skills.
- Bachelor’s degree in Computer Architecture, Computer Science, Electrical Engineering, or equivalent experience. A postgraduate degree is an advantage.
Additional Skills Considered a Plus
- Experience with visualization tools like Tableau and Looker.
- Hands-on experience with cloud computing and big data frameworks (e.g., GCP, AWS, Azure, Flink, Elasticsearch, and Beam).
- Knowledge of MLOps frameworks like TensorFlow Extended, Kubeflow, or MLFlow.
- Experience in Agile settings (e.g., Scrum).
Corporate Security Responsibility
All activities involving access to Mastercard assets, information, and networks come with inherent risks. 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 accessed information.
- Report any suspected information security violation or breach.
- Complete all periodic mandatory security training according to Mastercard’s guidelines.