As a data-driven strategy professional with experience in fintech, credit risk, and analytics, I specialise in leveraging data science, business intelligence, and strategic insights to drive growth and optimise decision-making. With a Machine Learning specialisation and an entrepreneurial mindset, I bring expertise in market analysis, pricing strategy, and performance optimisation across business functions. Passionate about using data to drive partnerships and innovation, I aim to contribute to high-impact strategic initiatives by combining analytical rigor with a deep understanding of business dynamics.
Company:
• MoneyView is a fintech start-up offering personal loans through a completely digital & paperless process.
Responsibilities:
• Led teams across new customer credit & currently overseeing policy for all 50+ lead-generating partners.
• Onboarded Tableau & led a dedicated team responsible for creating, maintaining & monitoring dashboards.
• Collaborated with Product & Tech teams for the seamless implementation of dozens of experiments.
• Played a key role in making credit architecture flexible by transitioning hard-coded rules into databases.
• Devised & implemented fraud defenses to reduce identity theft while balancing genuine customer impact.
• Actively participated in the hiring & training process, fostering the growth of high-performing individuals.
Reforms:
• Driving efforts to create a 100% resilient portfolio through repricing & valuations at micro-segmented level.
• Developed an ensemble ML model, leveraging the power of all data sources to streamline underwriting, improve rank ordering, & pricing, laying the groundwork for a framework change across programs.
• Drove small ticket PL business into profitability while contributing to 2.6x YOY credit-driven growth.
• Introduced foundational testing, challenging legacy policies to expand underwriting into new segments.
• Drafted policies for a "Low & grow" program for NTC customers by utilizing alternate data sources.
Introduced foundational testing, challenging legacy policies to expand underwriting into new segments.
• Drafted policies for a 'Low & grow' program for NTC customers by utilising alternate data sources.
• Created Python dashboard app hosted on AWS, enhancing automation, reporting & monitoring standards.
Languages:
Python: pandas, numpy, plotly, dash, scikit-learn, sqlalchemy
R Studio: dplyr, DBI
Tools:
Database Management: MySQL, Big Query
AWS: EC2, S3, Athena
Microsoft office: Excel, PowerPoint, Word
Visualisation: Tableau, Metabase
Confluence: Jira, Bitbucket
Analytics: Credit Risk Management, Portfolio Management
Data Science: Data analytics, Statistical modelling & Machine Learning