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(ref : hirist.tech)