Description

I work at the intersection of economics, data science, and technology, helping organisations develop better business strategies and decisions under uncertainty.

Across insurance, banking, and financial services, I’ve partnered with senior business leaders on problems influencing growth, risk, pricing, retention, product design, marketing, and performance. My focus is not AI/ML or GenAI for their own sake, but using economic reasoning and data to shape clear, actionable choices that impact outcomes.

With a PhD in Economics and hands-on experience building AI/ML models and GenAI products, I bridge the gap that often exists between strategy and data science for decision-making in various business functions.

I’ve led end-to-end analytics and decision-science initiatives spanning business challenge identification, problem framing, analysis design, model development and business interpretation of its output, contributing to process transformation, revenue growth, cost optimisation, risk management, product design, and performance improvements.

Industry field of expertise

Languages

English
Native or bilingual
Hindi
Native or bilingual

Workplace preferences

Remote only

Primarily works remotely

Independent GenAI Product Development
Generalist
August 2025 - Today (10 months)
• • Drawing from some pieces of my work in industry, I ideated and synthesised the following articles mapping some common business functions demonstrating how economic frameworks can guide data science to solve complex business problems: "Pricing as Strategy",
"Strategic Product Design", "Organisations as Incentive Structures", and "Risk as an Economic Decision Problem"
• • Ideated Effects of AI Investment on Economy, where using economics, I synthesised the then-scattered signals into a more comprehensive set of comments and questions that make the bigger picture clearer on the topic, allowing for possible directional inferences.
• • Developed Economic News Elaborator, and Academic Research Papers Summarizer: offline LLM-based tools using 7B LLM, RAG, ontology retrieval, and QLoRA finetuning fully developed locally on a personal laptop with 8GB GPU for local inference without using any cloud-based APIs or LLM orchestration framework.
Kotak Life
Deputy Vice President - Data Science
February 2024 - July 2025 (1 year and 5 months)
Mumbai, Maharashtra, India
Model Development & Risk Management:
• • Spearheaded the development of a segmentation-based attrition model using logistic regression in SAS, integrating behavioural economics principles to optimise retention strategies.
• • Designed a logistic regression model to predict claim risks, establishing a dynamic risk framework that enhances portfolio risk management benchmarked with a deep learning model in Python (Pytorch).

Predictive Analytics & Employee Optimisation:
• • Constructed predictive personas for employee hiring in distribution channels, utilising polynomial regression to refine interaction variables and boost model accuracy.
• • Led a three-month activation analysis for distribution channels using XGBoost in Python, improving long-term attrition forecasting and driving strategic decisions.
Data-Driven Insights & Strategic Recommendations:
• • Conducted econometric analysis on RDM performance data, establishing causal relationships with incentive structures, and providing data-driven insights to inform redesign initiatives.
• • Assessed the group secure capital product to determine the potential for reducing the capital guarantee period from one year to one day, optimising financial product offerings.

Market Analysis & Operational Efficiency:
• • Calculated market share through the analysis of public data, benchmarking new business performance against private and overall market trends to identify growth opportunities.
• • Ideated application of distance functions to identify duplicate customers with multiple policies, improving operational efficiency and reducing redundancies.
Team Leadership & Mentorship:
• • Mentored and trained junior team members on advanced modelling techniques, fostering a culture of continuous learning and excellence in data science.
Fractal | Remote
Engagement Manager - Data Science
March 2021 - August 2021 (5 months)
• • Led the review of industry reports and academic papers, identifying emerging insurance trends to inform business strategies.
• • Designed a value-based pricing framework for B2B2C sales, defining strategies to estimate & influence pricing within the value chain.
• • Uncovered critical consumption patterns in beverage sales data, enabling enhanced sales forecasting and trend analysis despite data limitations.
• • Evaluated survey data using Logistic Regression to determine psychological factors that significantly increased the likelihood of brand selection in consumer decisions.