Lead Data Scientist
Job ID: R-247932
Location: Navi Mumbai, Maharashtra, India – 400708
Experience Required: 8+ Years
Education: Bachelor’s / Master’s in Computer Science / Mathematics / Engineering / Statistics
Service Line: Software Engineering
Job Type: Full-Time
Role Overview
Join Mastercard’s Finicity team to lead innovation in the Open Banking space. As a Lead Data Scientist, you'll architect and implement scalable machine learning solutions for financial services including transaction classification, risk modeling, and intelligent decisioning. This role offers the opportunity to lead technical initiatives, mentor talent, and deliver high-impact products that enhance financial health and drive inclusive economic growth.
Key Responsibilities
Lead and mentor the data science team to deliver scalable, production-ready ML services.
Design and implement machine learning models for transaction analysis, financial risk evaluation, and temporal pattern recognition.
Validate, monitor, and enhance deployed ML models to ensure optimal business outcomes.
Guide the team in NLP, statistical modeling, and data science best practices.
Collaborate cross-functionally to identify use cases, define roadmaps, and communicate insights.
Translate complex technical findings into business-relevant narratives for leadership and clients.
Utilize containers, REST APIs, and event-driven architectures to deploy data science services.
Drive innovation by solving novel challenges in financial data science.
Contribute to infrastructure decisions including Kubernetes, Docker, and streaming pipelines.
Present technical ideas clearly in both written and spoken formats for various audiences.
Technical Requirements
Minimum 8+ years in data science and machine learning lifecycle management.
Strong experience with Python, TensorFlow, Scikit-learn, Pandas, SQL, and Jupyter.
Advanced proficiency in Natural Language Processing (NLP) and annotation-based data modeling.
Familiarity with transactional data, risk scoring models, and credit risk analytics.
Experience in designing and deploying REST APIs and containerized solutions using Docker and Kubernetes.
Proficiency in statistical modeling, feature engineering, and anomaly detection.
Hands-on exposure to modern MLOps, monitoring, and CI/CD for ML workflows.
Strong grasp of both structured and unstructured data pipelines.
Preferred Skills
FinTech or Financial Services experience highly preferred.
Background in building ML models for transaction classification, fraud detection, or financial identity verification.
Experience with event-driven architectures and tools like Kafka.
Strong problem-solving abilities and eagerness to drive innovation in new domains.
Prior experience managing and growing data science teams.
Corporate Culture & Values
Global-first, inclusive workplace with strong ethical leadership.
Passion for sustainability and empowering economic access.
Emphasis on creativity, autonomy, and continuous learning.
Transparent communication, innovation, and cross-disciplinary collaboration.
