Lead Data Scientist

Description :

Key Responsibilities :

- Apply AI/ML techniques to solve complex business problems across credit, fraud risk,

operations, collections, and customer service.

- Take end-to-end ownership of model lifecycle management, including design, development,

validation, implementation, monitoring, and updates.

- Lead, mentor, and manage a team of data scientists and analysts.

- Collaborate closely with business and functional stakeholders to ensure model outputs are actionable and aligned with business objectives.

- Ensure models adhere to best practices, governance standards, and regulatory requirements.

- Translate complex analytical results into insights and recommendations for leadership and stakeholders.

Required Skills & Qualifications :

- Modelling Expertise : Strong experience in model development and lifecycle management using supervised and unsupervised learning techniques, with structured and unstructured data (e.g., Risk Scorecards, Propensity Models, Optimization, NLP, etc.

- Programming Skills : Proficiency in Python (mandatory) and SQL for data extraction, analysis,

and model deployment.

- Analytical & Strategic Thinking : Exceptional logical reasoning, quantitative skills, and problem-solving capabilities.

- Communication Skills : Excellent written and oral communication skills with the ability to manage stakeholder expectations effectively.

- Business Acumen & Judgment : Strong understanding of business context and ability to balance innovative solutions with practical implementation.

- Self-Motivation : Comfortable working independently in ambiguous situations and driving initiatives to completion.

- Team Leadership : Experience managing, mentoring, and developing team members.

Educational Qualifications :

- B.Tech/B.E, B.Sc. in relevant fields (Computer Science, Statistics, Mathematics, or related disciplines).

Preferred Experience :

- 4 - 9 years of relevant experience in data science, machine learning, or analytics roles.

- Experience in financial services or risk management domains is a plus.

- Exposure to model governance, regulatory compliance, and production deployment of ML models