Lalit Sethia

About Lalit Sethia

Specialties: Economic analysis; Statistical and Causal inference; Econometric methods; Demand estimation; Machine learning; Time-series forecasting; Valuation modeling; Data visualization; Python; SQL; • Executed end-to-end projects in international economics, formulating hypotheses from complex research and testing them through rigorous research design, data collection, and empirical analysis; synthesized results into accessible presentations to communicate at workshops and conferences. • Extensive experience with Python (8y+), Stata (8y+), R (3y+), and SQL (2y+). Strong command over Excel and PowerPoint. • Gathered public and private data at various levels of aggregation through scraping, proposal submissions, collaboration, and collation; Obtained private data from the World Tourism Organization; Gathered unique data on Indian factories to track IT capital and investment; Worked closely with other researchers to create a novel dataset tracking international expansions by scraping SEC’s website. • Constructed intuitive and easy to understand metrics, as well as extensively dealt with mislabeled data and missing values; Constructed a metric to quantify demand risk at the industry level; Constructed a metric to track the reliance of factories on managerial input; Tracked errors in labeling and changes in the geographical boundaries of districts to construct a time-consistent dataset of Indian geography. • Employed statistical and causal inference techniques to extract robust statistical relationships between economic variables; Implemented an instrumental variable strategy in a quasi-experimental setting to show that high travel costs decrease international trade; Implemented a spatial difference-in-difference strategy to show that geographical barriers did not affect technology diffusion; Performed a within-industry analysis to show that large companies invested disproportionately more in IT than smaller companies; Implemented a high-dimensional regression model to discover a robust increase in demand risk with the upstreamness of an industry in the global value chain.• Taught data science with an emphasis on programming with Python, and best coding practices. Implemented various classification models including logistic regression, decision trees, feed-forward neural networks, and support vector machines. Looking for a skilled data scientist/quantitative researcher? Contact me at l••••@bu.edu

Experience

1. Senior Quantitative Analyst

   Truist

   Dec 2024 — Present

Education

• Indian Statistical Institute, Delhi Centre

  Master of Science (MS), Quantitative Economics

  2014 — 2016

• Boston University

  Doctor of Philosophy (PhD), Economics

• National Institute of Technology Warangal

  Bachelor of Technology, Biotechnology

  2007 — 2011

Skills

• Business Valuation
• Bloomberg
• Financial Analysis
• Data Analysis
• Investment Banking
• Equity Research
• Analysis
• Dcf
• C
• Financial Modeling
• Market Research
• C
• Valuation