Data Scientist (SVP)

We are looking for a highly accomplished and business-orientated senior vice president - data science - to lead our data science and advanced analytics function. The ideal candidate will have deep experience in the NBFC, FinTech, banking, or lending ecosystem, with a proven track record of building and deploying machine learning models that drive business growth, improve credit risk decisions, optimise collections, and enhance customer acquisition. This leadership role will be responsible for defining the organisation's AI and data science strategy, building high-performing teams, and partnering with business leaders to create data-driven competitive advantages across the lending lifecycle.

The core responsibilities for the job include the following:

Strategic Leadership:

• Define and execute the enterprise-wide Data Science and AI strategy aligned with business objectives.
• Drive data-led decision-making across credit underwriting, risk management, fraud prevention, collections, customer acquisition, and portfolio management.
• Partner with senior leadership to identify opportunities for leveraging advanced analytics and AI to improve business outcomes.
• Establish best practices, governance frameworks, and standards for model development and deployment.

Credit Risk and Lending Analytics:

• Lead the development and enhancement of credit underwriting models, application scorecards, behavioural scorecards, risk segmentation models, and early warning systems.
• Probability of Default (PD), Loss Given Default (LGD), and Exposure at Default (EAD) models.
• Develop strategies to improve portfolio quality while maximising approval rates and business growth.
• Drive analytics initiatives for risk-adjusted profitability and portfolio optimisation.

Customer Acquisition and Marketing Analytics:

• Build predictive models for customer acquisition, lead scoring, cross-sell, upsell, and customer lifetime value.
• Optimise marketing ROI through advanced targeting, segmentation, and campaign analytics.
• Utilise alternative data sources to improve customer onboarding and lending decisions.

Collections and Fraud Analytics:

• Develop collection propensity models and recovery optimisation strategies.
• Lead fraud detection and prevention initiatives using machine learning and advanced analytics.
• Implement real-time monitoring systems to identify risk and fraud patterns.

AI and Machine Learning Innovation:

• Drive the adoption of AI, machine learning, generative AI, and advanced analytics across business functions.
• Build scalable ML platforms and production-grade model deployment pipelines.
• Evaluate emerging technologies and identify opportunities for innovation.

Team Leadership:

• Build, mentor, and lead a high-performing team of data scientists, ML engineers, analysts, and data engineers.
• Foster a culture of experimentation, innovation, and continuous learning.
• Collaborate with Product, Engineering, Risk, Operations, Compliance, and Business teams.

Requirements:

• Bachelor's degree in engineering, mathematics, statistics, computer science, economics, or a related quantitative field.
• Master's degree (preferred) in data science, statistics, computer science, analytics, mathematics, or a related discipline.
• 12-17 years of experience in data science, analytics, risk analytics, or machine learning.
• Minimum 5+ years of leadership experience managing large data science teams.
• Strong experience in NBFC, FinTech, consumer lending, digital lending, banking, credit cards, or retail lending environments.
• Preferred Industry Background: NBFC, FinTech Lending, Digital Lending Platforms, Consumer Finance, Retail Banking, Credit Cards, Personal Loans, SME Lending, Buy Now Pay Later (BNPL).

Must Have:

• Advanced expertise in Python, SQL, R, Spark, and machine learning frameworks.
• Strong understanding of supervised and unsupervised learning techniques.
• Hands-on experience with the following: credit risk modelling, predictive analytics, fraud analytics, customer analytics, and collections analytics.
• Experience deploying models in production environments.
• Knowledge of cloud platforms such as AWS, Azure, or GCP.
• Familiarity with MLOps, model monitoring, and model governance frameworks.

Preferred:

• Experience with generative AI, LLMs, and AI-driven automation solutions.
• Knowledge of alternative data underwriting and digital lending ecosystems.
• Experience working with large-scale consumer lending portfolios.

Leadership Competencies:

• Strong business acumen with the ability to translate analytics into measurable business impact.
• Exceptional stakeholder management and executive communication skills.
• Proven ability to influence C-level executives and drive organisational change.
• Strategic thinker with strong execution capabilities.
• Ability to operate effectively in a fast-paced, high-growth environment.

Skills

ML, Python, Data Science, Machine Learning