Hiring for: One of India’s leading non-banking financial companies (NBFCs), focused on driving financial inclusion across rural and semi-urban markets.

Role: Team Lead – Data Science & Analytics - Emerging Business

Positions: 1

Experience: 10 to 15 years

Location(s): Mumbai

Type: On-site / Permanent

Salary: Up to INR 62 LPA (Including 20% variable pay)

JOB DESCRIPTION

Department: Data Science & Analytics

Reports To: Head – Data Science & Analytics

1. Job Purpose Statement

The Team Lead – Emerging Business will drive advanced analytics, machine learning, statistical modelling, and business intelligence initiatives across Risk, Collections, Fraud, Customer Analytics, Portfolio Management, and other strategic business functions.

The role will be responsible for delivering end-to-end analytical solutions, generating actionable business insights, ensuring model governance, and enabling data-driven decision-making across the organization. The incumbent will partner with business, risk, product, and technology teams to solve complex business problems and deliver measurable business impact.

2. Duties & Responsibilities

Data Science & Machine Learning

• Lead development of predictive, prescriptive, and machine learning solutions across Risk, Collections, Fraud, Customer Analytics, and Portfolio Management.
• Build and enhance scorecards, propensity models, forecasting models, segmentation frameworks, and optimization solutions.
• Apply advanced statistical and machine learning techniques to improve business performance and decision-making.
• Drive model monitoring, recalibration, and continuous performance improvement.
• Establish best practices for model development, validation, and deployment.

·      Design PAPQ strategies for targeted customer interventions.

Business Analytics & Strategic Insights

• Translate business challenges into analytical frameworks and actionable solutions.
• Conduct portfolio analytics, customer behavior analysis, profitability assessments, and performance diagnostics.
• Generate insights on customer acquisition, portfolio quality, collections performance, fraud trends, and business profitability.
• Identify growth opportunities, emerging risks, and operational inefficiencies through data analysis.
• Present strategic recommendations and business impact assessments to senior leadership.

Artificial Intelligence & Emerging Technologies

• Identify and implement relevant AI-driven solutions to improve business efficiency and decision-making.
• Leverage Generative AI for use cases such as reporting automation, knowledge management, and productivity enhancement.
• Evaluate emerging technologies and drive adoption where business value exists.
• Ensure AI initiatives align with governance, security, and compliance requirements.

Governance, Validation & Compliance

• Own model governance, validation, monitoring, and documentation processes.
• Ensure model performance through calibration, back-testing, stability assessment, and drift monitoring.
• Maintain audit-ready documentation and support regulatory reviews.
• Drive adherence to model risk management and data governance standards.

Deployment & MLOps

• Lead deployment and lifecycle management of analytical solutions.
• Partner with technology teams to establish scalable deployment and monitoring frameworks.
• Implement automated monitoring, retraining, and performance tracking mechanisms.
• Ensure production stability, scalability, and sustainability of deployed solutions.

Leadership & Team Management

• Lead, mentor, and develop Data Scientists and Analytics professionals.
• Drive capability building, knowledge sharing, and adoption of best practices.
• Manage project delivery, resource allocation, and team performance.
• Foster a culture of innovation, collaboration, and analytical excellence.

Stakeholder Management & Collaboration

• Partner with business and technology stakeholders to define analytical priorities and roadmaps.
• Communicate analytical findings and model outcomes in a business-friendly manner.
• Influence strategic decision-making through data-driven recommendations.
• Present analytical insights and performance updates to senior leadership.

3. Key Challenges

• Delivering scalable analytical solutions while maintaining governance and compliance standards.
• Balancing model performance, interpretability, and business applicability.
• Managing complex datasets and evolving business priorities.
• Driving adoption of analytics-led decision-making across business functions.
• Building and retaining high-performing analytics talent.

4. Decision Making Authority

Decisions Made Independently

• Selection of analytical methodologies, modeling techniques, and machine learning algorithms.
• Design of feature engineering strategies and analytical frameworks.
• Prioritization of analytical projects and allocation of team responsibilities.
• Establishment of analytical standards, governance processes, and best practices.
• Recommendation of analytics and AI use cases to address business challenges.

Decisions Made in Consultation with Manager

• Strategic analytics roadmap and transformation initiatives.
• Major model deployment approvals and business implementations.
• Policy changes impacting risk, collections, fraud, or portfolio strategies.
• Resource planning, capability-building, and governance approvals.

5. Job Requirements

Professional Qualification

• Bachelor's or Master's degree in Statistics, Mathematics, Economics, Engineering, Computer Science, Data Science, Analytics, or related disciplines.
• Preferred certifications in Machine Learning, Data Science, Artificial Intelligence, Cloud Technologies, or Advanced Analytics.

Experience

• 10 -15 years of experience in Data Science, Advanced Analytics, Machine Learning, Decision Science, Risk Analytics, or Business Analytics.
• Proven experience leading analytics teams and delivering large-scale analytical initiatives.
• Experience in Banking, NBFC, Financial Services, FinTech, or Digital Lending preferred.

Knowledge & Skills

• Statistical Modeling, Machine Learning, and Business Analytics.
• Python, PySpark,SQL and Data Visualization tools.
• Risk, Collections, Fraud, Customer, and Portfolio Analytics.
• Model Governance, Validation, Monitoring, and Deployment.
• Stakeholder Management, Leadership, Communication, and Problem Solving.