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Data / Ml Engineer-4
2 weeks ago
We work to connect and power an inclusive, digital economy that benefits everyone, everywhere by making transactions safe, simple, smart and accessible.
Using secure data and networks, partnerships and passion, our innovations and solutions help individuals, financial institutions, governments and businesses realize their greatest potential.
Our decency quotient, or DQ, drives our culture and everything we do inside and outside of our company. We cultivate aculture of inclusion
for all employees that respects their individual strengths, views, and experiences.
We believe that our differences enable us to be a better team - one that makes better decisions, drives innovation and delivers better business results.
Title and SummaryData / ML Engineer-4
Mastercard Overview
Mastercard is the global technology company behind the world's fastest payments processing network.
We are a vehicle for commerce, a connection to financial systems for the previously excluded, a technology innovation lab, and the home of Priceless.
We ensure every employee can be a part of something bigger and change lives. We believe as our company grows, so should you. We believe in connecting everyone to endless, priceless possibilities.Join a fast-growing team
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.
The challenge will be to create high-performance algorithms, cutting-edge analytical techniques including machine learning and artificial intelligence, and intuitive workflows that allow our users to derive insights from big data that in turn drive their businesses.
You will have the opportunity to create high-performance analytic solutions based on data sets measured in the billions of transactions and front-end visualizations to unleash the value of big data.
You will have the opportunity to develop data-driven innovative analytical solutions and identify opportunities to support business and client needs in a quantitative manner and facilitate informed recommendations/decisions through activities like building ML models, automated data pipelines, designing data architecture/schema, performing jobs in big data cluster by using different execution engines and program languages such as Hive/Impala, Python, Spark, R, etc.
Your Role- Drive the evolution of Data & Services products/platforms with an impactfocused on data science and engineering.
- Turning unstructured data into useful information by autotagging images and texttospeech conversions.
- Solving complex problems with multilayered data sets, as well as optimizing existing machine learning libraries and frameworks.
- Ensure proper data governance policies are followed by implementing or validating Data Lineage, Quality checks, classification, etc.
- Discover, ingest, and incorporate new sources of realtime, streaming, batch, and APIbased 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.
- Maintain awareness of relevant technical and product trends through selflearning/study, training classes and job shadowing.
- Participate in the development of data and analytic infrastructure for product development
- Partner with roles across the organization including consultants, engineering, and sales to determine the highest priority problems to solve
- Evaluate tradeoffs between many possible analytics solutions to a problem, taking into account usability, technical feasibility, timelines, and differing stakeholder opinions to make a decision
- Evangelize releases to users, incorporating feedback, and tracking usage to inform future development
- Work with small, crossfunctional teams to define the vision, establish team culture and processes
- Escalate technical errors or bugs detected in project work
- Maintain awareness of relevant technical and product trends through selflearning/study, training classes, and job shadowing.
- Support the building of scaled machine learning production systems by designing pipelines and engineering infrastructure.
- Experience and exposure to Python/Scala, Spark(tuning jobs), SQL, Hadoop platforms to build Big Data pro
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