Lead - Data Science

Key Responsibilities

• Lead End-To-End Model Development Lifecycle, including data preparation, model building, evaluation, and optimization
• Design and implement Scalable Data Pipelines (ETL/ELT) ensuring high data quality and reliability
• Architect and deploy Cloud-Based Machine Learning Solutions on Azure and AWS
• Develop Synthetic Data Solutions for testing and validation purposes
• Build and maintain Data Validation Frameworks, including schema validation and rule-based quality checks.
• Drive process standardization, automation, and optimization for repeatable workflows
• Create and maintain technical documentation and knowledge repositories
• Work within Agile frameworks (Scrum/Kanban) for efficient project delivery
• Leverage tools such as Databricks, Delta Lake, and MLflow for data and ML lifecycle management

Leadership & Stakeholder Management

• Lead, mentor, and coach a team of data scientists and engineers
• Own Sprint Planning, Backlog Management, and delivery timelines
• Collaborate with cross-functional teams including product, engineering, and business stakeholders
• Translating Business Requirements into Scalable Technical Solutions
• Present technical insights and architecture recommendations to senior leadership

Required Skills & Qualifications

• Strong expertise in Python Programming with libraries such as Pandas, NumPy, Scikit-learn, and PySpark
• Hands-on experience with data engineering concepts including ETL/ELT pipelines and data quality frameworks
• Proven experience in machine learning model development and deployment
• Experience with cloud platforms (Azure/AWS) for ML solutions
• Familiarity with Databricks ecosystem, including Delta Lake and MLflow
• Strong understanding of data validation, governance, and quality management practices
• Excellent problem-solving, analytical, and communication skills

Domain Expertise

• Experience in Finance and GRC (Governance, Risk, and Compliance) analytics
• Knowledge of risk modeling and regulatory compliance frameworks
• Understanding of AI governance and model lifecycle management