• Superior academic record at a leading national university in Computer Science, Computer Engineering, or Technology related field or equivalent work experience
• At least 12 years of experience as data engineer and with open-source tools
• At least 7 years of experience in managing people/teams
• In depth technical knowledge, drive, and ability to learn new technologies
• Outstanding communication skills (both verbal and written) and organizational skills
• Strong project management skills
• Strong hands-on experience in Analytics & Computer Science
• Demonstrated 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/Scala, Spark, Hadoop platforms and tools (Hive, Impala, Airflow, NiFi, Scoop), and SQL to build Big Data products & platforms
• Experience in building and deploying production level data driven applications and data processing workflows/pipelines and/or implementing machine learning systems at scale in Java, Scala, or Python and deliver analytics involving all phases like data ingestion, feature engineering, modeling, tuning, evaluating, monitoring, and presenting
• Curiosity, creativity, and excitement for technology and innovation
• Demonstrated quantitative and problem-solving abilities
• Ability to multi-task and strong attention to detail
• Motivation, flexibility, self-direction, and desire to thrive on small project teams

Desirable

• As a Director – Software Engineering AI/ML, you will develop data & analytics solutions that power the Mastercard customer experience and customer management enablement
• The challenge will be to create high-performance algorithms, innovative analytical techniques including machine learning and artificial intelligence, and intuitive workflows that allow our systems to leverage elegant prediction to drive business outcomes
• 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, design data architecture/schema, perform jobs in big data cluster by using different execution engines and program languages such as Hive/Impala, Python, Spark, R, etc
• Drive the evolution of products/platform with an impact focused on data science and engineering
• Participate in development of data and analytic infrastructure
• Participate in identification and evaluation of data elements to deliver analytics for the end-to-end customer experience
• Prototype new algorithms, experiment, evaluate and deliver actionable insights
• Continuously innovate and determine novel approaches, tools, techniques & technologies to solve business problems and generate business insights & recommendations
• Partner with stakeholders across the organization including consultants and engineering to determine the highest priority problems to solve
• Evaluate trade-offs between many analytics solutions to a problem, considering 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 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 data sets and conduct data manipulation and technical analysis leveraging industry techniques and best practices to identify trends and draw insights
• Clean and structure 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