Mastercard is the global technology company behind the world’s fastest payments processing network. Mastercard’s software engineering teams leverage Agile development principles, advanced development and design practices, and an obsession over security, reliability, and performance to deliver solutions that delight our customers.

Mastercard hiring fresher graduates with good programming skills, having proficient knowledge in standard software development, such as version control, testing, and deployment for the role of Software Data / ML Engineer.

Job Designation : Software Data / ML Engineer

Salary : 21 LPA – 28 LPA

Qualification :  Bachelor’s / Master’s degree

Experience : Freshers – 2 years

Skill Set :

1. Good programming skills in Python/Scala, Spark(tuning jobs).
2. Proficient in standard software development, such as version control, testing, and deployment.
3. Good knowledge of SQL, Hadoop platforms to build Big Data products & platforms.
4. Good knowledge in MLOps frameworks such as TensorFlow Extended, Kubeflow, or MLFlow.
5. Basic knowledge of statistical analytical techniques, coding, and data engineering.
6. Familiar with cloud computing and big data frameworks e.g. GCP, AWS, Azure, Flink, Elasticsearch, and Beam
7. Experience participating in complex engineering projects in an Agile setting e.g. Scrum.
8. Experience with visualization tools like tableau, looker.
9. Experience with data pipeline and workflow management tools: NIFI, Airflow
10. Motivation, flexibility, self-direction, and desire to thrive on small project teams.
11. Strong relationship, collaboration skills, and organizational skills.
12. Good communication skills – both verbal and written.

Job Description :

As a Data / ML Engineer in the Data Engineering & Analytics team, you will develop data & analytics solutions that sit atop vast datasets gathered by retail stores, restaurants, banks, and other consumer-focused companies.

1. Drive the evolution of Data & Services products/platforms with an impact-focused on data science and engineering.
2. Turning unstructured data into useful information by auto-tagging images and text-to-speech conversions.
3. Solving complex problems with multi-layered data sets, as well as optimizing existing machine learning libraries and frameworks.
4. Provide support for deployed data applications and analytical models by being a trusted advisor to Data Scientists and other data consumers by identifying data problems and guiding issue resolution with partner Data Engineers and source data providers.
5. Ensure proper data governance policies are followed by implementing or validating Data Lineage, Quality checks, classification, etc.
6. Discover, ingest, and incorporate new sources of real-time, streaming, batch, and API-based data into our platform to enhance the insights we get from running tests and expand the ways and properties on which we can test Experiment with new tools to streamline the development, testing, deployment, and running of our data pipelines.
7. Maintain awareness of relevant technical and product trends through self-learning/study, training classes and job shadowing.
8. Participate in the development of data and analytic infrastructure for product development
   Continuously innovate and determine new approaches, tools, techniques & technologies to solve business problems and generate business insights & recommendations.
9. Partner with roles across the organization including consultants, engineering, and sales to determine the highest priority problems to solve.
10. Evaluate trade-offs between many possible analytics solutions to a problem, taking into account usability, technical feasibility, timelines, and differing stakeholder opinions to make a decision.
11. Break large solutions into smaller, releasable milestones to collect data and feedback from product managers, clients, and other stakeholders.
12. Evangelize releases to users, incorporating feedback, and tracking usage to inform future development
    Ensure proper data governance policies are followed by implementing or validating Data Lineage, Quality checks, classification, etc.
13. Work with small, cross-functional teams to define the vision, establish team culture and processes
    Consistently focus on key drivers of organization value and prioritize operational activities accordingly.
14. Escalate technical errors or bugs detected in project work.
15. Maintain awareness of relevant technical and product trends through self-learning/study, training classes, and job shadowing.
16. Support the building of scaled machine learning production systems by designing pipelines and engineering infrastructure.

Location : Pune, India