Head of Data Science

Role Summary

The role provides strategic leadership for all data science, advanced analytics, and AI initiatives at the bank. Reporting directly to the COO, the position is responsible for driving enterprise wide analytical intelligence, strengthening digital lending performance, and aligning data science capabilities with the bank's digital banking transformation roadmap. The role integrates global neo bank practices, modern product constructs, and real time decisioning models to accelerate operational excellence and customer centricity.

Key Responsibilities
Strategic and Functional Leadership

• Lead the end to end data science agenda under the COO's transformation and operational excellence mandate.
• Develop a forward looking analytics strategy that aligns operational efficiency, digital scalability, and product innovation.
• Drive adoption of analytics driven culture across business, credit, risk, and operations.

Digital Lending and Risk Analytics

• Build, deploy, and continuously refine credit scoring models for nano, micro, consumer, and SME lending.
• Enhance real time underwriting engines through alternate data, psychometric scoring, device intelligence, and behavioral analytics.
• Strengthen model governance processes covering testing, monitoring, compliance, and regulatory expectations.
• Partner with Credit Risk and Digital Product teams to optimize approval rates and portfolio quality.

Neo Banking and Global Best Practices

• Translate global challenger bank and neo bank models into locally relevant use cases.

• Introduce insights led product lines such as

• credit builder products,

• instant dynamic credit limits,
• PFM and spend analytics,
• embedded finance solutions,
• micro savings and investment tools,

• real time fraud detection frameworks.

• Drive benchmarking against high performing digital only banks to accelerate transformation.

AI, ML, and Advanced Analytics

• Lead advanced AI initiatives including customer lifecycle modeling, LLM based insights, anomaly detection, and NLP based automation.
• Build scalable ML pipelines with strong MLOps, cloud infrastructure, and data engineering alignment.
• Strengthen operational intelligence through predictive modeling for collections, servicing, fraud, and operational optimization.

Cross Functional Collaboration

• Work closely with the COO, CIO, Chief Digital Officer, Chief Risk Officer, and product leadership to embed analytics across all critical workflows.
• Engage with SBP and internal audit teams on model governance, transparency, and risk controls.
• Provide the COO with analytics-based decision support, simulations, and operational performance insights.

People Leadership

• Build a high performing team of data scientists, ML engineers, analysts, and model validators.
• Establish best practices for experimentation, lifecycle management, documentation, and quality assurance.
• Drive capability building initiatives to uplift data literacy across operations and business teams.

Qualifications and Experience

• Master's or PhD in Data Science, AI, Computer Science, Statistics, or equivalent quantitative discipline.
• 8 to 12 years of relevant experience in fintech, digital banking, microfinance, or analytics driven transformation.
• Proven experience in digital lending models, decision engines, and real time ML.
• Strong understanding of global neo banking models and product architectures.
• Hands on experience with Python, SQL, cloud environments, ML frameworks, and MLOps stacks.

Core Competencies

• Strategic mindset with strong operational orientation.
• High ownership, execution discipline, and stakeholder engagement capability.
• Deep expertise in analytics, modeling, and AI driven transformation.
• Ability to operate in a fast moving and evolving digital banking environment.