Mastercard is hiring for a fresher entry level data engineer / ML engineer job

Position:

• Role: Data / ML Engineer-4

Company:

• Name: Mastercard

Location:

• City: Pune
• Country: India
• Postal Code: 411006

Job Type:

• Nature: Full-time

Job Mode:

• Arrangement: Hybrid

Job Requisition ID:

• Identifier: R-203166

Years of Experience:

• Entry level (0-3 years)
  

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Company Description

• Global Leader in Payments: Mastercard operates the world’s fastest payment processing network, connecting billions of people worldwide.
• Driving Digital Economy: Our mission is to foster an inclusive digital economy that benefits everyone, making transactions safe and accessible.
• Innovation Hub: As a technology innovation hub, we connect financial systems and provide services to previously underserved communities.
• Cultural Values: We believe in inclusivity and respect for individual strengths, which enables better decision-making and drives innovation.
• Commitment to Growth: We are committed to employee growth, offering opportunities to be part of something bigger and positively impact lives.
• Innovative Solutions: We focus on developing solutions that help individuals, financial institutions, governments, and businesses achieve their full potential.

Profile Overview

• Role Overview: The Data / ML Engineer-4 will be part of the Data Engineering & Analytics team, focusing on developing solutions that leverage large datasets from various sectors.
• Key Responsibilities:
  • Data Analytics Solutions: Develop high-performance algorithms and analytical techniques, including machine learning and AI.
  • Big Data Insights: Create solutions to derive insights from vast datasets to drive business value.
  • Innovative Solutions: Identify opportunities for data-driven decisions through machine learning models and automated data pipelines.
  • Data Integration: Incorporate real-time, streaming, batch, and API-based data sources into the platform.
  • Collaboration: Work with cross-functional teams to define project vision and culture.
  • Technical Trends: Stay updated with relevant technical and product trends through continuous learning and training.

Qualifications

• Technical Skills:
  • Programming Languages: Proficiency in Python, Scala, Spark, SQL, and experience with Hadoop platforms.
  • Data Tools: Experience with data pipeline tools like NIFI and Airflow.
  • Automation: Skill in developing shell scripts for automation tasks.
  • Software Development: Understanding of version control, testing, and deployment processes.
  • Statistical Techniques: Basic knowledge of statistical analytical techniques and data engineering principles.
• Soft Skills:
  • Problem Solving: Strong quantitative and problem-solving abilities.
  • Communication: Excellent verbal and written communication skills.
  • Team Collaboration: Ability to work well in small project teams.
  • Adaptability: Flexibility and self-motivation to thrive in dynamic environments.
• Educational Background:
  • Degree: At least a Bachelor’s degree in Computer Science, Computer Architecture, Electrical Engineering, or equivalent experience. A postgraduate degree is advantageous.
• Additional Skills:
  • Visualization Tools: Experience with tools like Tableau and Looker.
  • Cloud Computing: Hands-on experience with cloud computing frameworks such as GCP, AWS, Azure, and big data frameworks.
  • MLOps Knowledge: Familiarity with MLOps frameworks like TensorFlow Extended, Kubeflow, or MLFlow.
  • Agile Experience: Experience in Agile methodologies like Scrum.

Additional Information

• Data Governance: Ensure data governance policies are followed, including data lineage and quality checks.
• Product Development: Participate in the development of data and analytic infrastructure.
• Innovation Focus: Continuously seek new approaches, tools, and technologies to solve business problems.
• Security Compliance: Adhere to Mastercard’s security policies and practices, ensuring the confidentiality and integrity of information.
• Mandatory Training: Complete all required security training as per company guidelines.

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Job Responsibilities

• Platform Evolution:
  • Focus on data science and engineering to evolve Data & Services products and platforms.
  • Implement new tools to streamline the development and deployment of data pipelines.
• Data Transformation:
  • Convert unstructured data into actionable information, such as auto-tagging images and text-to-speech conversion.
• Problem Solving:
  • Tackle complex problems involving multi-layered datasets.
  • Optimize existing machine learning libraries and frameworks.
• Data Application Support:
  • Provide support for data applications and analytical models.
  • Act as a trusted advisor for Data Scientists and data consumers.
• Data Integration:
  • Discover and integrate new data sources to enhance platform insights.
• Technical Awareness:
  • Stay informed about technical and product trends through continuous learning.
• Infrastructure Development:
  • Contribute to the development of data and analytics infrastructure.
  • Innovate to solve business problems and generate insights.
• Cross-Functional Collaboration:
  • Partner with consultants, engineers, and sales teams to prioritize problems.
  • Evaluate analytics solutions based on usability, feasibility, and stakeholder feedback.
• Solution Implementation:
  • Break down large solutions into smaller milestones for iterative feedback and development.
  • Promote new releases and incorporate user feedback to guide future development.
• Data Governance Compliance:
  • Ensure adherence to data governance policies, including lineage and quality checks.
• Team Leadership:
  • Define vision, culture, and processes for small, cross-functional teams.
  • Focus on key drivers of organizational value and prioritize activities.
• Error Escalation:
  • Report technical errors or bugs encountered during projects.
• Learning and Development:
  • Engage in self-learning, training, and job shadowing to remain updated on technical trends.
• Machine Learning Systems:
  • Design pipelines and infrastructure to support scaled machine learning production systems.

Corporate Security Responsibility

• Security Policies:
  • Follow Mastercard’s security policies to manage risks associated with accessing company assets.
• Confidentiality:
  • Ensure the confidentiality and integrity of accessed information.
• Violation Reporting:
  • Report any suspected security violations or breaches.
• Training Compliance:
  • Complete mandatory security training sessions as per company guidelines.

Please click here to apply.

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