Data Science Engineer

Our Purpose

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 a

culture 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.

Job Title

Data Science Engineer

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 Science Engineer II on 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 impact focused on data science and engineering
- Participate in the development of data and analytic infrastructure for product development
- Participate in identification and evaluation of data elements to deliver analytics for product development
- Prototype new algorithms, experiment, evaluate and deliver actionable insights.
- Continuously innovate and determine new approaches, tools, techniques & technologies to solve business problems and generate business insights & recommendations
- Partner with roles across the organization including consultants, engineering, and sales to determine the highest priority problems to solve
- 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
- Break large solutions into smaller, releasable milestones to collect data and feedback from product managers, clients, and other stakeholders
- 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.
- 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
- Identify and act upon product improvement opportunities
- Aggregate and collate client data sets and conduct data manipulation and technical analysis leveraging industry techniques and best practices to identify trends and draw insights
- Clean and structured data sets fed to downstream analytics
- Escalate technical errors or bugs detected in project work
- Maintain awareness of relevant technical and product trends through self-learning/study, training classes, and job shadowing.
- Superior academic record at a leading national university in Computer Science, Computer Engineering or Technology related field or equivalent work experience
- Demonstrated basic knowledge of statistical analytical techniques, coding, and data engineering
- Experience leveraging open source tools, predictive analytics, machine learning, Advanced Statistics, and other data techniques to perform basic analyses
- High proficiency in using Python/Spark, Hadoop platforms & tools (Hive, Impala, Airflow, NiFi), SQL to build Big Data products & platforms
- Curiosity, crea