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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)


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.

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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