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.