Manager, Software Engineering (Data Science, People Management)

Our Purpose

_Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, we’re helping build a sustainable economy where everyone can prosper. We support a wide range of digital payments choices, making transactions secure, simple, smart and accessible. Our technology and innovation, partnerships and networks combine to deliver a unique set of products and services that help people, businesses and governments realize their greatest potential.

_Title And Summary

Manager, Software Engineering (Data Science, People Management)

Who is Mastercard?

Mastercard is a global technology company in the payments industry. Our mission is 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. With connections across more than 210 countries and territories, we are building a sustainable world that unlocks priceless possibilities for all.

Role

The Commercial Expense Reports and Analytics department provides data-driven insights and analytics capabilities for issuers and corporations all over the world.

This position will lead a team of employees and contractors in their career growth. Mentorship, problem resolution, and team-building will be essential to succeed in this role. This position will primarily focus on data science and analytics projects, ensuring high-quality, secure, and scalable solutions for data processing, modelling, and insights generation based on technical/functional requirements and business needs. Responsible for assuring the daily activities of the team meet the requirements of SLA’s.

Team members may be data scientists, data engineers, analysts, and architects. The team is cross-functional, which means that between all its members they possess sufficient skills to do the work.

Responsibilities

• Provide ongoing guidance to teams on professionalism, career growth, development standards, automation, and tools and processes such as Agile, Lean, etc.
• Work with Product Management and Engineering to translate product use cases and requirements into analytics-driven design, predictive models, and advanced visualization implementations. Influence the product owner to balance the business and technical priorities and the value of the architecture work.
• Lead the design, development, and optimization of data science workflows and machine learning pipelines to deliver predictive and prescriptive insights.
• Collaborate with data engineers to optimize ETL pipelines, data ingestion processes, and database design for analytics needs.
• Collaborate with business and technical partners to identify and scope the opportunities, outline potential value and ROI, identify risks, benefits, and constraints.
• Execute and own the baseline data science implementations, including feature engineering, statistical analysis, and machine learning model development in collaboration with the team.
• Document data models, machine learning workflows, and analytics outputs as per development best practices. Follow Mastercard quality assurance and quality control processes, managing security best practices in data management, ML models, and analytics architecture.
• Ensure robust test coverage for machine learning models, data processing pipelines, and integration with application layers.
• Support defect management, versioning, and deployment of analytics models and data workflows.
• Develop strategic plans to integrate AI/ML capabilities and advanced analytics into business workflows and reporting systems.
• Perform feature/enhancement estimates, demonstrations, and related activities.
• Support production-level analytics pipelines and machine learning model monitoring, including root cause analysis and issue resolution.
• Regularly communicate with management about risk analysis, design trade-offs, and the business impact of analytics solutions.
• Participate & contribute to analytics-focused product planning sessions and requirement workshops for domain-based solutions, technical feasibility, and resource requirements.
• Participate in model performance reviews and code reviews for key data science functionalities.

All About You

Essential Knowledge, Skills, and Experience

• Proven experience leading data science and analytics teams in a geographically distributed and/or remote work environment.
• Track record demonstrating the ability to build a diverse and strong team – attracting, hiring, retaining, and motivating the best talent.
• Strong technical expertise in machine learning, deep learning, Generative AI, data analysis, and statistical modelling, with proficiency in Python, R, or SQL.
• Experience providing innovative analytics and data-driven solutions that have lasting impact in multiple dimensions; strategic, operational, and financial.
• Knowledge of data science concepts, including working with structured and unstructured data, delivering machine learning models, NLP solutions, and analytics-driven features.
• Flexible and responsive, with the ability to adapt to rapid change in direction or business priority.
• Openness to feedback and strong self-awareness.
• Ability to work within the confines of data governance, model explainability, and privacy standards.
• Strong problem-solving abilities involving data integration, model optimization, and visualization techniques.

Desirable

• Experience managing and analysing structured and unstructured data using tools such as Python, R, or SQL.
• Experience with distributed databases or big data platforms such as Snowflake, Databricks or Spark.
• Familiarity with ML frameworks such as TensorFlow, PyTorch, or Scikit-learn.
• Experience in data visualization tools (like Tableau, Power BI, and visualization libraries of python).
• Experience with Agile/Scrum environment preferred.

Corporate Security Responsibility

All Activities Involving Access To Mastercard Assets, Information, And Networks Comes With An Inherent Risk To The Organization And, Therefore, It Is Expected That Every Person Working For, Or On Behalf Of, Mastercard Is Responsible For Information Security And Must:

• Abide by Mastercard’s security policies and practices;
• Ensure the confidentiality and integrity of the information being accessed;
• Report any suspected information security violation or breach, and
• Complete all periodic mandatory security trainings in accordance with Mastercard’s guidelines.