Data Science Vice President

Role Summary

This leadership role is responsible for establishing and scaling the bank's Data Science and AI capabilities, driving advanced analytics, digital lending performance, and enterprise-wide decision intelligence. Reporting to the COO, the position plays a central role in the bank's transition toward a digital banking ecosystem by embedding global neo bank practices, advanced AI models, and high-value analytical solutions across customer, product, and risk domains.

Key Responsibilities

Strategic and Functional Leadership

Set up and lead the Data Science and AI function from the ground up, including team structure, hiring, mentoring, and operational frameworks.

Design and execute a comprehensive analytics and AI strategy aligned with the COO's operational excellence agenda.

Shape the organisation's 3 to 5 year AI roadmap, including data ecosystem readiness, AI and ML use case pipeline, and adoption of emerging techniques such as GenAI.

Drive AI literacy and a data-driven culture by coaching cross-functional teams, conducting training sessions, and simplifying analytical concepts for senior leadership.

Digital Lending and Risk Analytics

Design and deploy AI-based credit scoring and risk decisioning frameworks for nano, micro, consumer, and SME lending, in alignment with Risk and Business teams.

Build real-time underwriting engines integrating alternate data, psychometric inputs, and behavioral signals.

Strengthen model governance, performance monitoring, and regulatory compliance for all lending and risk-related models.

Customer Lifecycle, Personalization, and Engagement

Develop predictive and prescriptive models across the customer lifecycle, including acquisition, engagement, churn, cross-sell, and collections.

Embed lifecycle models into campaigns, customer journeys, and growth strategies in collaboration with Digital, Marketing, and CRM teams.

Build and scale personalization and recommendation engines, including segmentation, microsegmentation, and intelligent product recommendations, to enhance customer stickiness and product adoption.

Neo Banking and Global Best Practices

Translate global neo bank and challenger bank practices into scalable local use cases.

Evaluate digital-only banking product constructs such as PFM tools, dynamic credit limits, embedded finance, spend intelligence, micro savings, and instant onboarding journeys.

Integrate global learnings into the local digital banking strategy, ensuring alignment with regulatory frameworks and market needs.

AI, ML, and Advanced Analytics

Lead development and deployment of real-time ML systems covering underwriting, fraud detection, portfolio optimization, collections prioritization, and operational efficiency.

Partner with Business, Product, IT, and Compliance to convert use cases into production-grade AI solutions with measurable commercial outcomes.

Build robust ML pipelines with strong MLOps, model lifecycle practices, and cloud-aligned architecture.

Cross-Functional Collaboration

Work closely with the COO, CTO, Chief Digital Officer, Chief Risk Officer, Product Heads, and Engineering teams to operationalize analytics across all critical workflows.

Provide the COO with data-driven insights, performance intelligence, and scenario modeling to support transformation decisions.

Lead analytics engagements with regulatory bodies, internal audit, and governance committees.

People Leadership

Build and mentor a multidisciplinary team comprising data scientists, ML engineers, analysts, and model validators.

Establish high-quality documentation, experimentation practices, and model lifecycle governance.

Create structured learning pathways to uplift analytics capability across business and operations.

Qualifications and Experience

Master's or PhD in Data Science, AI, Computer Science, Statistics, or a related quantitative field.

8 to 12 years of experience in fintech, digital banking, microfinance, or advanced analytics with leadership responsibility.

Demonstrated expertise in AI-based digital lending models, credit scoring, and real-time decisioning.

Strong understanding of global neo-bank operating models and innovative product structures.

Hands-on experience with Python, SQL, ML frameworks, cloud platforms, and MLOps technologies.

Core Competencies

Strategic thinking with an operational transformation mindset.

Strong commercial and analytical orientation.

High ownership, collaboration capability, and disciplined execution.

Ability to influence senior stakeholders and operate effectively in a fast-paced digital environment.